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metallurgist & mineral processing engineer

For its extensive practical experience, 911 Metallurgisthas a clear understanding of what successful mineral processing engineering is and how to go about achieving it. Your goal is the production of a material that is marketable and returns you and your investors sustainable revenues.

Although improvements to the metallurgical processes have been made over the years the fact is that the unit operations, the machines, those too often called black boxes involved have not evolved or changed much since inception. Ore is reduced in size, chemicals are added and minerals separated and upgraded to produce a marketable product. Much of this process is mechanical and generally mistaken for some dark alchemy.We are the Anti-Alchemists.

Our vast experience has been gained through operation and start-up of both small and large scale mining/metallurgical operations in a range of commodities in thebase metals (Cu, Pb, Zn) and theprecious metals (Au, Ag,)

A solid metallurgist understands, the most important aspect of an operating process is its stability. Simple to say, but generally the most ignored in mineral processing. Linked unit operations require each to be stable, and each contains a different set of variables that have to be contended with. Thanks to some degree of stability: operating changes can be made and evaluated; increases in throughput can be made; and equipment performance improved. The more complicated the processes become, the more difficult it is to achieve and maintain stability. In mineral processing, unlike most processing operations, we have limited control of the main input, the feed ore. In most cases this inherently is variable and usually outside of the processors control.

Because you are too close to your own story, you might not see the forest for the trees and have chaos mistaken for stability. We, you, and your group have been battling plant problems for weeks, you start to accept chaos as a daily state of affair and consider it your new stability.

Each mineral processing plant is different: with varied ore types, mining equipment, and management (operating) philosophy. The evaluation and prioritisation of variables that affect the plant performance is the primary function. Implementing changes within the constraints imposed can be difficult, as resources may be limited.

Invariably the ability to solve problems can be confusing due the large numbers of variables that may impact the processes. In most cases problems are not metallurgical in nature but rather operational and mechanical. Problem solving is a process and in many operations this ability is absent. All too often many changes are made together without a solution resulting, on more confusion. Most plants learn to live or survive their problems, not to solve them.

Our engineering team has a global experience in the mining industry across all facets of the mine life-cycle. Our focus is to add value to your project and company by understanding your needs, employing innovative ideas and applying sound engineering while maintaining an economically driven approach. We have a combination of senior level professionals, experienced project managers, and technical staff to execute projects efficiently. We work in a partnership with our clients to achieve their company goals and operational milestones in a timely and cost effective manner.

environmental health situation in nigeria: current status and future needs - sciencedirect

Environmental health-related risks are becoming a primary concern in Nigeria, with diverse environmental problems such as air pollution, water pollution, oil spillage, deforestation, desertification, erosion, and flooding (due to inadequate drainage systems) caused mostly by anthropogenic activities. This paper reviews the pre-existing and current environmental health problems, proffer future research and needs, policy needs, and recommendations necessary to mitigate Nigeria's environmental health situation. Data from the Institute of Health Metric and Evaluation on Global Burden of Disease (GBD) was used to ascertain the causes of Death and Disability-adjusted Life Years (DALYs) in Nigeria from 2007-2017 and published literatures where reviewed. According to the world health data report, most of the highest-ranked causes of DALYs in Nigeria are related to environmental risk factors. The lower respiratory infection associated with air pollution has advanced from the 4th in 2007 to the highest ranked cause of death in 2017. Other predominant causes of death associated with environmental risk factors include chronic respiratory diseases, cardiovascular diseases, enteric infections, diarrheal diseases, communicable, maternal, neonatal, and nutritional disease, which has resulted in approximately 800 thousand deaths and 26 million people living with DALYs per annum in Nigeria. Major environmental risk factors include household air pollution, ambient air pollution, water, sanitation, and hygiene (WaSH), which shows a prolonged but progressive decline. In contrast, ambient particulate matter pollution, ambient ozone pollution, and lead exposure show a steady rise associated with death and DALYs in Nigeria, indicating a significant concern in an environmental health-related risk situation. Sustaining a healthy environment is critical in improving the quality of life and the span of a healthy life. Therefore, environmentally sustainable development policies and practices should be essential to the population and policymakers for a healthy life.

mineralogical and technological aspects of phosphate ore processing | springerlink

The article studies the mineralogical features of phosphate ores. In the conditions of declining industrial reserves of apatite-containing ores, issues of a more comprehensive and in-depth study of the mineral and material composition, as well as the improvement of existing technologies for the processing of this type of raw material, become topical. Using optical methods of analysis, electron microscopy with automated mineralogical analysis (MLA), mineral and elemental composition of apatite was obtained. Taking into account the studied mineralogical and material composition, experiments on grinding and flotation were carried out. Based on these data, it was concluded that the optimal scheme for the processing of phosphate ores is a flotation scheme with preliminary selective disintegration.

Phosphates are one of the most important minerals on Earth, as they are used as fertilizers for agriculture and as a necessary raw material for the chemical industry (Brylyakov 2004; Abouzeid 2007). In addition, phosphates are the source of rare-earth elements. They are used in many commercial and industrial products, such as: detergents, toothpastes and fireproof materials. Worldwide consumption of P2O5 in all of the areas above is projected to grow gradually from 44.5 million tons in 2016 to 48.9 million tons in 2020 (Jasinski 2017).

In the conditions of declining industrial high-quality reserves of phosphorus-containing ores, issues of a more comprehensive and in-depth study of the mineral and material composition, as well as improvement of existing technologies for processing this type of raw material, become urgent. The study of the influence of the mineral raw materials composition on the features of the beneficiation schemes construction is given in the works of many authors (Aleksandrova et al. 2012; Evdokimova et al. 2012; Gerasimova et al. 2018; Litvintsev et al. 2006; Mitrofanova et al. 2017).

According to the mineralogical analysis, the main primary minerals of ANO are apatite and nepheline, the contents of which are respectively 30.67 and 30.88%; minor quantities contain pyroxenes, mica, feldspars, as well as natrolite and kaolinite secondary minerals formed due to the destruction of the primary mineral phases. Phosphorus-containing minerals of the sample are apatite, eschynite, phosphates of rare-earth elements and lomonosovite, with a distribution to these minerals of 99.94, 0.01, 0.02 and 0.03% phosphorus respectively. Valuable minerals of the sample are apatite the main mineral concentrating phosphorus and nepheline the main mineral concentrating aluminum.

Image: (a) - in reflected light; (b) - in backscattered electrons. Apatite is characterized by a pronounced idiomorphism of grains that have clear crystallographic outlines; the shape of apatite grains is columnar, prismatic, acicular, which causes a weak connection between them in aggregates. The crystalline form of apatite, the natural brittleness of the mineral will contribute to the primary destruction of the mineral during ore grinding and the concentration of apatite in smaller grades.

As a result of the grinding process study, it was found that with an increase in the grinding time, the particle size sharply decreases, and pulp grinding with a solid content of 50% gives the best results. Studies have also been conducted for the process of grinding apatite ore with the addition of tributyl phosphate in amount of 500 and 1000ml/ton. Studies have shown that the addition of surface-active substances (tributyl phosphate) during grinding of ANO and PO does not only increase the efficiency of grinding, but also partially convert rare-earth metals into soluble form with their subsequent extraction.

In the conditions of declining quality of industrial reserves of phosphate ores, issues of a more comprehensive and in-depth study of the mineral and material composition, as well as the improvement of existing technologies for the processing of this type of raw material become topical. Achieving this goal is complicated by the constant decline in the quality of ores involved in processing and requirement of 100% recycled water supply implementation. Based on the mineralogical, chemical, material composition, as well as technological research on the possibility of processing phosphate ores, it was concluded that the optimal scheme for the extraction of apatite is a flotation circuit with preliminary selective disintegration. At the same time, the possibility of extracting rare earth metals has been established.

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

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the mining sector of liberia: current practices and environmental challenges | springerlink

Liberia is endowed with an impressive stock of mineral reserves and has traditionally relied on mining, namely iron ore, gold, and diamonds, as a major source of income. The recent growth in the mining sector has the potential to contribute significantly to employment, income generation, and infrastructure development. However, the development of these mineral resources has significant environmental impacts that often go unnoticed. This paper presents an overview of the Liberian mining sector from historical, current development, and economic perspectives. The efforts made by government to address issues of environmental management and sustainable development expressed in national and international frameworks, as well as some of the environmental challenges in the mining sector are analyzed. A case study was conducted on one of the iron ore mines (China Union Bong Mines Investment) to analyze the effects of the water quality on the local water environment. The results show that the analyzed water sample concentrations were all above the WHO and Liberia water standard Class I guidelines for drinking water. Finally the paper examines the application of water footprint from a life cycle perspective in the Liberian mining sector and suggests some policy options for water resources management.

After the discovery of high-grade iron ore in Bomi Hills, Bong, and Nimba, natural resources have been the basis of Liberian economy and its people livelihood. Iron ore mining was the mainstay of the Liberian economy between 1960 and 1980, contributing more than 60% of export earnings and about 25% of GDP (Boakye et al. 2012), which then ranked Liberia as the largest exporter of iron in Africa and third largest in the world. Gold and diamond mining in Liberia was carried out largely by alluvial mining of small-scale operations, with estimates of over 100,000 artisanal miners in Liberia. But nearly 14years of war (19892003) destroyed much of the countrys productive infrastructure and brought mining to a virtual halt. Liberia is estimated to hold reserves ranging from between two to five billion metric tons of iron ore and three million ounces of gold (Boakye et al. 2012). The major mineral commodities produced in Liberia are iron ore, gold, and diamond. Mining concessions cover an operational area of 113,256ha (Ministry of Finance 2013). Besides the production of iron ore, gold, and diamond, Liberia remains largely unexplored and has shown other minerals such as beryl, tin, columbite-tantalite, phosphates, zinc, copper, lead, rare earth minerals, nickel, molybdenum, beach sand (zircon, rutile, ilmenite, and monazite), bauxite, kyanite, chromite, uranium, and silica sands. All are characteristically associated with Precambrian/Proterozoic rocks which underlie most of the country.

Since the cessation of hostilities, revival of the mining industry has been an explicit government objective in its efforts to reconstruct the country and to underpin growth by leveraging Liberias rich natural resources to the extent of attracting massive foreign investment of USD 7.6 billion and creating about 10,000 jobs (LEITI 2016; Ministry of Finance 2013). Investments comprise, among others, rehabilitation of old and installation of new mining plants, construction of railways, roads, and bridges. As a result, the Government of Liberia has enacted (20032006) a legal framework providing for the sustainable use and conservation of natural resources. However, the adoption of the environmental management tools such as Environmental Impact Assessment (EIA), Environmental Impact Statement (EIS), and the harnessing of best practices valorizing local knowledge are still lacking. Thus, the pressure on the environment is still heavy.

Industrial mining in Liberia includes gold and iron ore. The mining of these minerals is associated with huge environmental impacts ranging from land form degradation, pollution of air quality, loss of biodiversity, and watercourse contamination. The latter is of serious challenge in the mining sector because of the climatic condition of Liberia. For instance, the rehabilitated Nimba mine is estimated to generate 150 million tonnes of waste rocks (60Mm3 of waste rocks) over its first 20years mines life (AcerlorMittal Liberia Limited 2013). In addition, the New Liberty Gold mining operations require 1.2Mm3 of water annually and its tailings storage facility (TSF) is expected to discharge 9.4Mm3 (received from rainfall and runoff during the wet season) of water annually into surface water streams (Aureus Mining 2014). Therefore, there are greatest potential impacts on water quality, human health, and ecosystem from these activities coupled with increased sediment load due to the high erosion potential of soil when disturbed and effluents discharged of toxic substances, such as cyanides and heavy metals including acid mine drainage (AMD) that can cause long-term impairment to watercourses and biodiversity (Akcil and Koldas 2006). Additionally, the data collection for environmental information has been decentralized among various line ministries and agencies, as well as international organizations and institutions which have a stake in the Liberian environment.

Based on the current development trend, the objective of this paper is to carry out a systematic review of the current water use-related challenges in the Liberian mining sector particularly the water management issues and mining regulatory frameworks. Results from the review will be used to recommend policy strategies that promote sustainable water resources management in the mining sector. Accordingly, Historic and economic perspectives of the Liberian mining sector of this paper discusses the historical and economic contribution to the economy. Mineral legislation, regulatory framework, and environmental challenges in the Liberian mining sector describes the current mining methods, legislative frameworks, and environmental challenges while Policy suggestions presents a case study and water resource management challenges. Conclusion presents policy suggestions and concludes with some recommendations.

Liberia is a leading country in mineral resources with substantial iron ore, gold, and diamond deposits. Iron ore mining was previously undertaken by American and European companies in the areas of Bomi Hills, Bong Mines, Mano River, and Nimba. Those concessions resulted in widespread clearance of tropical rainforest for mines (open-cast pits), processing plants, housing and roads, railways, and unmanaged deposit sites. The Nimba mine for instance produced some 300 million tons of mining wastes (unwanted materials) that were deposited in the surrounding forest. Environmental impact assessments had not been conducted at the sites and potential risks were unknown.

Statistics of government revenues by sector contribution indicates that the mining sector contributed to 53% (USD 53.38 million) of the total revenues during the FY14/15 (Fig. 1) and generating about 10,000 jobs. In the same year, the sector faced a drop in demand, production level, investment, and loss of employment as a result of the twin shockEbola virus disease and the price of iron ore. The value of the sector production in 2014 was USD 78.85 million (58.3%). Figures 2 to 3 shows the production of gold and diamond and iron ore from 2012 to 2015 (LEITI 2016).

There are different categories of mining activities in the Liberian mining sector, including artisanal/small-scale miners (ASM), medium size domestic enterprises, large-scale mining, and exploration companies. Currently, there are 1293 mining operations in the country (MLME 2010), of which 1142 (88.3%) are ASM, 65 (5%) are medium size, 78 (6%) are exploration companies, and 8 (0.6%) are large-scale enterprises. Among the large scale companies, the main producers are Arcelor Mittal (iron ore), China Union Investment (iron ore), MNG Gold Inc. (gold), and Aureus Mining Inc. (gold). The ASM are also involved in the extraction of gold and diamond. However, the development of these ASM operations is limited because of lack of resources and infrastructure. Consequently, there are inadequate information available on the ASMs and medium size enterprises in detailing their processes and economic activities. Nevertheless, the compilation and research of this information is a necessary activity. Table 1 shows the total productions of the large-scale mining companies operating in the Liberia.

The Ministry of Lands, Mines, and Energy (MLME) is the Government Agency responsible for the administration of the mineral and mining sector, including granting mining licenses, and it has statutory oversight of the energy, land, minerals, and water sectors. The minerals and mining sector is regulated by the Mining and Minerals Law of 2000 and Exploration Regulations (MLME 2010). The Minerals Policy of Liberia was created in March 2010 to complement the Mining and Minerals Law. These documents outline the Governments expectations with regard to the contributions of all stakeholders in the sustainable development of Liberias mineral resources. These laws are under review, but outline five types of mining licenses (Table 2). In addition to the mining licenses, there is a Mineral Development Agreement (MDA), which sets out the basis to acquire a class A mining license. The MDA sets out in detail the operational and fiscal terms for both exploration and mining and to ensure a straightforward transition from exploration to the mining phase of the operation provided that the operator has complied with the general provisions of the law. In negotiating an MDA, the Minerals Technical Committee has discretionary authority regarding those matters which are subject to the regulations, which together with the law specify principal terms and conditions.

Table 2 and Fig. 4 outline the various types of mining licenses and the procedures required to obtain the licenses, respectively. The duration, land size, and applicants eligibility are also indicated.

Currently there are four large-scale industrial mines (two in gold production and two producing iron ore) operating in Liberia with several others into exploration and mine development for both gold, but mainly iron ore (Table 1). Open cut mining method is generally employed by the operators. The ore is extracted from the mine and processed through the plant to produce a concentrate. Tailings, or waste material, are then deposited in a tailing storage facility (TSF). The gold operation employs the conventional carbon-in-leach (CIL) method, which comprises of the following:

These mining and mineral processing technologies require sufficient energy, water, and chemical reagents as sources for operations; thus polluting groundwater, watercourses, and habitats from spills and leakages of toxic or hazardous substances significantly.

The principal agency for the management of the environment in Liberia is the Environmental Protection Agency (EPA). The Environmental Protection Agency Act of Liberia (EPA 2003) mandates the EPA to coordinate, monitor, and supervise all activities in the field of the environment. The EPA makes mandatory to file an Environmental Impacts Assessment (EIA) and Environmental Impacts Statement (EIS) to obtain government approval prior to initiating activities. In the case of the mining sector, an EIA declaration format has been specifically designed for mining activities. The EIA has five component phases: namely, project screening; scoping; description of the project/action, alternatives, and environmental baseline; identification of environmental impacts; environmental management plan/design of corrective measures; and monitoring and control. This EIA process is similar to other EIA processes worldwide in that the EIA is a process that analyzes and evaluates the impacts that human activities can have on the environment. Also, its purpose is to guarantee a sustainable development that is in harmony with human welfare and the conservation of ecosystems; thus, proven itself to be an effective tool in environmental planning and management (Jay et al. 2007; Ortolano and Shepherd 1995; Toro et al. 2010; Wathern 1994; Wood 1993).

The laws in place for mining operations in Liberia, therefore, tend to be broad and ineffective. Additionally, there are overlaps and conflicts between different pieces of legislation (e.g., Public Procurement and Concession Act and the Minerals and Mining Law of 2000) that govern the sector. Furthermore, data collection is mainly carried out by various governmental bodies concerned with environment protection and policy (Forestry Development Authority, Ministry of Lands, Mines, and Energy). Besides, basic environment statistics such as water resources (surface and ground water abstraction, water used by sectors, freshwater availability, precipitation, evapotranspiration, water quality, river inflow/outflow) and land degradation information are mostly not available. Some available data are of limited time and geographical coverage. Those data often result from case studies or projects of limited duration. After the study or project ended, data collection usually stopped. Other available data are not up-to-date; consequently, hindering data collection and reporting processes. The lack of adequate logistics, personnel, and funding also constrain proper governance, particularly in relation to field monitoring and technical audit functions.

Water use in the mining sector and its associated environmental impacts have not been properly investigated. Although, the large-scale mines are in their early stages of operations and are located in and around major river courses and its tributaries. Presently, the gold mines in Liberia use cyanide in the recovery process, cyanide leaching is the standard method used for recovering approximately 83% of most gold throughout the world today (Karahan et al. 2006). Also, the uncontrolled management of cyanide when comes in contact with waterways has serious environmental and health consequences. In recent time there have been public outcry by local mining communities of contaminated drinking water sources (streams, creeks) from mining concessions in the country.

Water resource management is one of the greatest global challenges of the twenty-first century (Boccaletti et al. 2010). The mining industry and water resources are critically linked; mining needs substantive amounts of water to proceed but can also have major impacts on surface and ground water resources. Given waters primary role in sustaining ecosystem, communities, and economies, the mining industry is recognizing the challenges posed by sustainable water resources management and is embracing the opportunities it presents (Mudd 2008). In contrast to the abundance of mineral wealth in Liberia, water resources are vulnerable to environmental impacts from mining activities. Unless appropriate corrective actions are taken, the mining sector is expected to place further degradation on the countrys undeveloped water resources.

It is also expected that ore grade will steadily decline as high grade ores are preferentially mines (Mudd 2010; Mudd and Weng 2012); and the decline in ore grade has large ramifications regarding the potential environmental impacts of mining wastes (Northey et al. 2013). However, different mining methods and mineral processing techniques have unique water requirements. Therefore, reduction in ore grade will require more improved technologies and energy consumptions. These activities will in turn affect water quality through erosion and sedimentation, contamination with heavy metals, acid rock drainage, chemical contamination, and sewage and microbial contamination. The average grade of iron ore in Liberia currently is relatively high (up to 60% Fe see Table 1), but will gradually decline as mining progresses, therefore, requiring sinter/concentrator plant for beneficiation of saleable concentrates.

China Union (Liberia) Bong Mines InvestmentBong Minesis a subsidiary of China-Union Hong Kong Mining Company, Ltd. (Wuhan Iron and Steel Corporation) located in Bong County, Central Liberia. The mine is situated 10 13 38 N and 6 48 0 E and located 78km northeast of Monrovia, the capital city of Liberia (Fig. 5). The total area of the mining concession is 610km2 surrounded by over 20 towns including cultural sites, some of which were relocated due to impacts of the mining operations. The mean annual rainfall of the concession area is approximately 2700mm. The company started production in early 2014 to June 2015, but later suspended operations in late 2015 as a result of the twin shockEbola virus disease in Liberia and the price of iron ore.

The mine site is located in an area that has many small surface water bodies that are used by communities for drinking and fishing and support aquatic life; thus, predicted to have the potential to alter the flow properties and degrade the water quality of the surface water bodies. The affected water bodies include Wadea Creek, Yia Creek, Wea Creek, and the St. Paul River Basin. Runoff (4.5m3/s) from the waste rock dump (WRD) discharges into the Wadea Creek while overflows (10.6m3/s) from the tailings dam discharges into the Wea Creek. The Yia Creek is located north of the tailings dam. These creeks flowing downstream of the mine drain into the St. Paul River, which is the primary water source for the mine and is located approximately 10km northwest of the mine.

On the 18th of November 2016, a total of six 1.5L grab water samples, four (4) from surface water (SP1-SP4) and two (2) from ground water (SP5-SP6). Samples SP1 and SP2 were collected from the pump station and tailings dam respectively, which overflows discharge into the Yia and Wea creeks. Samples SP3 and SP4 were taken respectively from upstream and downstream of Wadea Creek while SP5 and SP6 were taken from the Botota and Gorzue communities located near the mine. These samples were taken to the laboratory of the College of Environmental Science and Engineering in Tongji University as shown in Table 3. Figure 6 shows the water sample points of collection.

Photographs of the water sample point collection of China Union; S surface water sample; G ground water sample; S1: Pump Station; S2: Tailing Dam; S3: Upstream Waydea; S4: Downstream Waydea; G1: Well, Botota Town; G2: Borehole, Gorsue Town

Laboratory analyses were carried out to assess the concentrations of physical and chemical water quality parameters of each sample gathered. Some parameters that were analyzed for determining pollution loads from point and non-point sources were limited to total dissolved solid (TDS), turbidity, electrical conductivity (EC), total organic carbon (TOC), Fe, Al, As, Zn, P, V, B, Ba, Ca, and Cr, mainly because of their relevance as water quality indicators.

Despite the prolong suspension until now, heavy metals (e.g., iron, aluminum, calcium, and zinc) are present in high concentrations, but with some variations due to the sample points location relative to the mine. Also, the inception of groundwater aquifer with abandon mine pits may have minimum contribution. The measurement results of these samples were compared to drinking standards instead of surface water quality standard for two main reasons as follows: firstly, the water bodies are used directly as drinking and domestic water by communities located in and around the mine, which is evident by some of the sample points; secondly, there is no surface water quality standard for Liberia. Also, with respect to some of the sample point locations, technically, it is not appropriate to compare the measurement results of sample points (SP1, SP2, and SP3) with a drinking water standard because at this location, the water is not directly consumed by the communities in and around the mine, but it is the source of their drinking water at sample points (SP4, SP5, and SP6). Therefore, the results should be view from the perspective of those sample points collected from drinking water sources.

The Fe and TDS concentrations in all of the water sample exceed the WHO and Liberia Water Standard Class I (drinking water) guidelines (Fig. 7a, b) respectively. Aluminum, boron, and calcium were also present in high concentrations in some of the water samples (Fig. 7d) as well as phosphorus and zinc which were found in all of the water samples. The concentration of chromium in sample 6 exceeds the Liberia Water Standard Class I (drinking water) guidelines (Fig. 7e). Accumulation of these heavy metals, which formed in association trace elements lead to carcinogen, diarrhea, etc. and acid rock drainage (ARD). The concentrations of turbidity, TOC, and EC are shown in (Fig. 7c). Despite the short term of operations, the arsenic concentration in some of the samples is at the level of WHO and LSW Class I standards for drinking water. Arsenic is known for its toxicity in both human and the environment when it accumulates. The TOC concentration is extremely high in sample points (SP4, SP5, and SP6) because of shifting cultivation farming done by the communities while as the high concentration turbidity in sample points (SP1, SP2, and SP3) is due to the fact that those points are located near the mines likewise the concentration of EC (Fig. 7f).

Water quality monitoring. a. Compare the concentration of iron with LWS Class I. b Compare the concentration of TDS with LWS Class I. c Concentration of EC and TDS in the water samples. d, e Concentrations of the various metals in the water samples. f Concentration of turbidity and TOC

It can be suggested that the mining operations, when stringent water management is not taken, will significantly impact the local water environment and have health consequences. Aquatic habitat and water users from villages located within and around the mine concession are the main receptors for the project. Aquatic habitat can be affected by changes in water quality, changes in channel morphology induced by changes in stream hydrology (driven by sediment transport) as well as changes to the flow regime itself. Human receptors are potentially affected by changes in surface water quality and water availability. Secondary effects can result from any aquatic habitat impacts that affect fishing.

A number of environmental assessment tools and methodologies have been developed by the scientific community in recent years to assess water use and related impacts of the mining industry. One of the methods for assessing water use on a life cycle basis that is probably most widely used is the water footprint approach developed by (Hoekstra et al. 2009). This alignment was reflected in the development of the international standard, ISO 14046 water footprintprinciples, requirements, and guidelines, which defines a water footprint as a metric that quantifies the potential environmental impacts related to water (ISO 2014).

Despite the large environmental impacts associated with the mining industry, there have been relatively few attempts to quantify water-related impacts from the industry using these methods. For instance, CSIRO Mineral (Norgate and Jahanshahi 2004; Norgate et al. 2004a; Norgate et al. 2004b; Norgate and Rankin 2000; Norgate and Rankin 2001; Norgate and Rankin 2002) and others (Giurco et al. 2000; Giurco et al. 2006; Lunt et al. 2002; Van Berkel 2000) used LCA methodology to assess the environmental impacts of various metal production processes practiced either currently or potentially in Australia. (Northey et al. 2016) identified a range of opportunities and few limitations on the use of water footprint assessments in mining industry. Among the opportunities are water footprint and LCA can be used to provide a more holistic assessment of the benefits and drawbacks of technologies being developed and deployed in the mining industry through the consideration of indirect (supply chain) impacts; improve the usefulness and relevance of water related data disclosures that are presented by corporate sustainability reports. Particularly for companies that have facilities in multiple regions with differing water contexts.

The development of water footprints of mined products is heavily dependent upon rigorously quantified estimates of the flows of water into and out of production processes, and the quality of water associated with these flows (Northey et al. 2015). The Minerals Council of Australia and the University of Queensland recently developed the Water Accounting Framework for the Minerals Industry that provides a method for individual mining companies to consistently record and report water flow, quality, and storage data for their individual operations (Mineral Council of Australia (MCA) 2012). Overtime, the increased adoption of this framework should lead to improvements in the quality and availability of data that can be used in water footprint assessments. Measuring water use and assessing its environmental impacts in the Liberian mining sector, particularly on a life cycle basis, are therefore important first steps towards sustainable mining in the sector. These statistical information could be used in the preparation of the National State of the Environment reports.

The mining sector is vital to Liberias economic and social development, owed largely to the endowment of its natural resources. The sector contribution to the GDP will continue to grow as new discoveries are made and the development of new mines. The Mineral and Mining Law needs to be updated to address contemporary technical, legal, and regulatory issues; thus requires empowering the EPA, other line ministries and agencies to adequately monitor and regulate the mineral and mining sectors. It is recommended that the ASM should be organized into a cooperative for proper management and accountability.

Adopting the Water Accounting Framework for the Minerals Industry and water footprint from a life cycle assessment perspective will ensure all mining companies consistently record and report water outflow, intake, quality, and storage data; quantity and quality of water discharged in water courses; and recycle and reuse water in process plants in the Liberian mining sector with respect to water resources management.

Giurco D, Stewart M, Petrie J (2000) The role of LCA in performance assessment in minerals processinga copper case study Environmental issues and management of waste in energy and mineral production:267273

Norgate T, Rankin W (2001) Greenhouse gas emissions from aluminium productiona life cycle approach Greenhouse gases in the metallurgical industries: policies, abatement and treatment,(Met Soc CIM), Toronto 89

Norgate T, Rankin W (2002) An environmental assessment of lead and zinc production processes. In: Proceedings, green processing: international conference on the sustainable processing of minerals. Australasian Institute of Mining & Metallurgy, Cairns, pp 177184

Northey S, Mudd G, Haque N The challenges of estimating the water footprint of mined commodities. In: Dynamic ecolibrium: sustainable engineering society conference SENG (2015),Engineers Australia, p 62

Wilson, S.T.K., Wang, H., Kabenge, M. et al. The mining sector of Liberia: current practices and environmental challenges. Environ Sci Pollut Res 24, 1871118720 (2017). https://doi.org/10.1007/s11356-017-9647-4

techno-economic evaluation of microalgae high-density liquid fuel production at 12 international locations | biotechnology for biofuels | full text

Microalgae-based high-density fuels offer an efficient and environmental pathway towards decarbonization of the transport sector and could be produced as part of a globally distributed network without competing with food systems for arable land. Variations in climatic and economic conditions significantly impact the economic feasibility and productivity of such fuel systems, requiring harmonized technoeconomic assessments to identify important conditions required for commercial scale up.

Here, our previously validated Techno-economic and Lifecycle Analysis (TELCA) platform was extended to provide a direct performance comparison of microalgae diesel production at 12 international locations with variable climatic and economic settings. For each location, historical weather data, and jurisdiction-specific policy and economic inputs were used to simulate algal productivity, evaporation rates, harvest regime, CapEx and OpEx, interest and tax under location-specific operational parameters optimized for Minimum Diesel Selling Price (MDSP, US$ L1). The economic feasibility, production capacity and CO2-eq emissions of a defined 500ha algae-based diesel production facility is reported for each.

Under a for-profit business model, 10 of the 12 locations achieved a minimum diesel selling price (MDSP) under US$ 1.85L1 / US$ 6.99 gal1. At a fixed theoretical MDSP of US$ 2 L1 (US$ 7.57 gal1) these locations could achieve a profitable Internal Rate of Return (IRR) of 9.522.1%. Under a public utility model (0% profit, 0% tax) eight locations delivered cost-competitive renewable diesel at an MDSP of

The public utility approach could reduce the fuel price toward cost-competitiveness, providing a key step on the path to a profitable fully commercial renewable fuel industry by attracting the investment needed to advance technology and commercial biorefinery co-production options. Governments adoption of such an approach could accelerate decarbonization, improve fuel security, and help support a local COVID-19 economic recovery. This study highlights the benefits and limitations of different factors at each location (e.g., climate, labour costs, policy, C-credits) in terms of the development of the technologyproviding insights on how governments, investors and industry can drive the technology forward.

In 2018, global energy consumption grew at twice the average rate recorded in 2010 [1], driven by a growing economy valued at US$ 136 trillion [2] and increased heating and cooling demands [1]. Despite global commitments on climate action, significant growth in renewables failed to keep pace with energy demand, resulting in a rise in greenhouse gas emissions (GHGs). Previously, the OECD called upon governments to develop enabling policy frameworks that will catalyze private sector investment to drive the large-scale transformation needed for a low carbon energy sector [3]. Substantial progress in renewable wind and solar PV technologies is driving a significant increase in renewable electricity supply and, coupled with battery technologies, is also transitioning the small vehicles market. However, high-density liquid fuels are critically underdeveloped and are expected to remain essential for the heavy transport, aviation, shipping, and logistics sectors for the foreseeable future, which combined, account for 12.7% of global energy demand [4]. As these fuels account for approximately 10% of global anthropogenic CO2 emissions [5], the development of low carbon alternative fuels is essential to meet international COP21 Paris CO2 emission reduction commitments and UN Sustainable Development Goals [6].

Advanced microalgae-based renewable fuel systems have significant potential to address these needs and to support a globally distributed and dispatchable fuel network to contribute to political, economic, social, environmental, fuel and climate security [7]. Current first-generation biofuel technologies, reliant on food crops, such as bioethanol from corn or sugar and biodiesel from soy or palm oil, compete with food production for arable land and fresh water and contribute to eutrophication [8, 9]. In contrast, microalgae systems can utilize saltwater and/or nutrient-rich wastewater and be deployed on non-arable land or in the oceans. These factors, coupled with high solar conversion efficiencies, can tap into the abundance of available solar energy (~3000 ZJyear1 or ~5000global energy demand) to capture CO2, provide feedstocks for renewable fuel production, and expand global photosynthetic productivity. Ringsmuth et al. [10] estimated that supply of global diesel, aviation and shipping fuel needs could theoretically be provided by microalgae-based fuel production [4, 10] using only 0.18% of global surface area [10]less than 10% of the area currently used by agriculture.

Advancing microalgae-based fuel technologies to a sustainable and commercial scale requires detailed and robust techno-economic and lifecycle analysis. This, in turn, is critical to attract an appropriate share of the renewable energy investment pool (cumulative US2.9 trillion since 2004) [11] that can advance the technology further. It can also support governments to define key areas of policy development, more quickly [12].

A number of reported models have evaluated the potential of algae-based renewable fuel systems [12,13,14,15,16,17,18,19,20,21,22,23,24]. Such studies have considered the effects of factors related to climate (e.g., solar radiation, temperature); operating conditions (e.g., nutrients, mixing regime, light regime, cell density); biology (growth, light tolerance, metabolic profile); or processes (e.g., harvest regime, fuel conversion method) on output variables categorized by: productivity (e.g., photosynthetic conversion efficiency, biomass yield, lipid yield or biofuel yield); economic feasibility (e.g., internal rate of return (IRR), minimum selling price (MSP); environmental performance (e.g., energy return on energy invested (EROEI), CO2 emissions per unit energy, life cycle analyses); scalability; or a combination thereof.

Naturally, the key determinants of economic feasibility are to produce the most fuel at the minimum cost. Selection of appropriate locations to establish microalgae-based biofuel production facilities is, therefore, critical due to the dual effects of climatic conditions on algae growth and production potential, and widely differing economic and policy settings between jurisdictions that effect the production cost.

Comparisons between locations, to date, have mostly assessed the productivity potential of microalgae systems as a function of climatic variables, particularly solar radiation [22, 24] and temperature [24, 25]. For example, Moody and co-authors (2014) integrated historical meteorological data with a growth model to evaluate lipid productivity of Nannochloropsis at 4388 global locations and reported the highest annual average lipid yields to be in the range of 24 and 27m3ha1year1, in Australia, Brazil, Colombia, Egypt, Ethiopia, India, Kenya, and Saudi Arabia [26]. In contrast, techno-economic assessments (TEA) evaluate the economic feasibility and often combine process-based modelling related to reactor or facility designs and technologies with economic input values. Many TEAs are limited to one climatic zone or several climatic zones within one jurisdiction. For example, a study by Davis et al. [24] modelled the costs, resource requirements and emissions for production of five billion gallons of fuel at various locations across the US. Biomass peak productivities of up to 2530gm2day1 were assumed to be achievable and fuel produced at a minimum diesel selling price (MDSP) of

In general, wide variations between model assumptions and approaches has made it difficult to compare like with like, to identify the most suitable systems, processes, and locations for deployment at scale. A comprehensive review of algae-based biofuel models by Quinn and Davis [27] emphasized the importance of harmonized assessments to enable direct comparisons, and highlighted the need to consider the exact location of the production plant which has important impacts on productivity, CapEx, OpEx [28] as well as financial inputs. Our recent work confirmed these findings and further revealed the critical influence of policy settings which vary markedly across global jurisdictions [12]. The lack of harmonization in current assessments has resulted in large discrepancies between estimated algae-based renewable fuel costs that range from US$ 0.43 L1 (US$ 1.64 gal1) to over US$ 7.92 L1(US$ 30.00 gal1) [27].

This study builds on Roles et al. [12] to address this critical knowledge gap by benchmarking the economic feasibility of microalgae-based biodiesel production across 12 international locations to identify important conditions required for commercial scale up. The specific objectives of this study were to:

Determine the lowest theoretical Minimum Diesel Selling Price (MDSP) based on the 12 locations analyzed, compare the range in MDSP variations across these sites and explore a process for the identification of promising locations for global microalgae fuel production.

Our analysis accounts for critical location-dependent variables that affect production capacity, production cost and net emissions. It is based on extensive work on the development and validation of our integrated Techno-Economic and Life Cycle Assessment (TELCA) model of the microalgae liquid fuel production facility detailed in Roles et al. [12] (see also Additional file 1). This work demonstrated an economic, energy-efficient, and low CO2 emission pathway to deliver micro-algae-based high-density liquid fuels through a combination of technology, scale, policy and location-specific cost settings. The study highlighted the critical importance of factors other than technological advancements on the economic feasibility of fuel productionin particular, the role of policy settings. Here, our simulation is extended with location-specific inputs to provide a techno-economic evaluation of microalgae-based high-density liquid fuel production across a diverse range of locations and jurisdictions at a commercially optimized scale of 500 ha total pond area (see Methods and Additional file 1). Actual temporally and spatially resolved weather data including solar radiation, temperature, and humidity were used as inputs to enable dynamic modelling of biomass productivity and evaporation. Materials, labour costs, tax and interest rates were applied for each jurisdiction. The analysis provides a direct performance comparison of a well-defined microalgae renewable diesel production system [29] across 12 locations distributed throughout six continents, and covering a broad range of climatic (Graphical abstract, temperate to tropical) and economic conditions (Table 2). A base system was fixed for all locations, while process modelling was used to optimize a range of operational settings to improve the economics for each location including: strain selection, pond depth, culture density, harvesting regime and water sourcing.

Significantly, we identify important operational factors that can be improved for individual locations to increase productivity while driving down price and emissions; evaluate the impact of different economic and policy settings between jurisdictions and demonstrate the use of our TELCA platform to assist in model guided systems optimization to de-risk scale up and support business development.

All techno-economic analyses are limited by the quality of the input data, the assumptions made, and the calculations conducted. Extensive work has previously been completed to validate the input data, the response of each process module, subprocesses and the whole process described by the 500ha renewable high-density liquid fuel production facility [12]. Additional file 1 details the simulation used, and within it, Section 4 provides the model validation. Following internal data, module, subprocess and process validation, the TELCA model was next validated against a broad range of independent techno-economic and life-cycle analyses (Additional file 1: Figure S26). Of these, we consider the NREL model [13] (Additional file 1: Figure S26) to be the most comprehensive. Given the complexity of our TELCA model and that of the NREL model, and the fact that when set to the same production conditions they yielded a mean diesel selling Price within 1% of one another, we conclude that the NREL and TELCA models independently validate each other. This analysis not only confirms the robustness of TELCA but also of the NREL model. Finally, we conducted validation against an operational demonstration scale 0.4ha microalgae production facility; the TELCA simulation of this facility identified the facilities CapEx to within 5% of the actual construction cost. Indeed, the TELCA evaluation delivered a calculated CapEx cost 5% above the actual construction costs suggesting that the assumptions were reasonably conservative (i.e., US$ 52.5 m2) at the 0.4ha scale (i.e., US$ 525,000ha1).

The Algae Productivity Model incorporated into TELCA 2, here (Fig.1) enables a more dynamic evaluation of spatiotemporal effects on the biological response of algae which is a critical determinant of success. One limitation of this study was the extrapolation of reported algae growth parameters to outdoor conditions. We recognize that such an approach does not take into account the many other potential factors that can affect productivity in natural systems, such as grazing, contamination, and culture crash, nor does the input weather data take into account severe weather events. However, it also does not include future improvements. The average annual values that we have calculated and used for our analyses range from 8.6 to 22.1gm2day1 and these productivities have been shown to be achievable in long-term outdoor experimental conditions [25, 30]. Future perspectives of this model are to integrate long-term actual productivity data.

Overview of analytical framework. a Techno-economic calculation scheme. b Microalgae-based renewable diesel production process flow diagram and model inputs (modified from [12]). International Location Specific Environmental Inputs (green) and the Algae Productivity Model (orange) connect with the high-rate pond module of TELCA, to enable location, system, and strain specific growth modelling (1h temporal resolution). Location Specific Economic Inputs (blue, top right) influence the final minimum diesel selling price and internal rate of return

The economic feasibility, biodiesel production capacity as well as embodied and process associated greenhouse gas (GHG) emissions were evaluated for 12 international locations using an expanded version of our previously reported Techno-Economic and Life Cycle Analysis (TELCA) tool (Fig.1a, b) [29]. The updated TELCA2 simulation used for this study is described in detail in Additional file 1. It includes:

location specific economic inputs (Table 2; Additional file 1: Section 1), such as the costings of capital and operational expenditure, interest, labour and tax (Fig.1b; blue, Additional file 1: Section 1.1).

Two business case scenarios at each location were assessed: a standard commercial for-profit business model (Scenario 1); and a public utility not-for-profit model (Scenario 2). For Scenario 1, economic feasibility was calculated using the Internal Rate of Return (IRR, %) based on the difference between the MDSP and a fixed theoretical diesel selling price of US$ 2 L1. For Scenarios 1 and 2, the MDSP is reported (Table 3). Microalgae-based biodiesel production capacity is defined as kL diesel ha1year1 based on optimized conditions for biomass production and harvesting regimes which resulted in the lowest MDSP.

CO2 emissions were calculated from CO2 (gCO2eq MJ1) absorbed during the overall photosynthetic biomass production and fuel production processes, offset against the amount of fossil-based CO2 released during construction (e.g., via embodied emissions in the construction, equipment supply and supply of consumable items), operation of the facility (external CO2 supply for biomass production11% CO2 concentration (Additional file 1: Appendix 1), and embodied CO2 emissions in the production and supply of nutrients), as well as emissions from subsequent fuel use. To minimise emissions, the model has been structured around a fully self-sufficient energy design (i.e., all of the energy required to operate the plant including electro-flocculation and hydrogen production was produced internally with solar PV (Additional file 1: Section3). All emissions have been fully incorporated into the net energy and CO2 accounting, and balanced over the productive life of the facility (30years). Reduction in emissions was assessed as the difference between the overall emissions from the process and emission from conventional fossil-based diesel fuel production and use, that it displaces (i.e., displaced fossil fuel (gCO2eq MJ1)renewable diesel (gCO2eq MJ1)=CO2 emission reductions (gCO2eq MJ1).

Simulations were performed for a facility comprising 177 high-rate microalgae production ponds of 4.27ha each, (total pond area=500ha), on-site harvest, processing and refining facilities (Fig.1b, Additional file 1). Algal biomass was harvested using electro-flocculation and concentrated via centrifugation (Additional file 1: Section3), before being converted to crude oil via hydrothermal liquefaction (HTL) with a biomass to green crude conversion of 55% [12, 31, 32] (Additional file 1: Appendix 1). Renewable diesel was refined using conventional hydrotreatment/hydrocracking and fractionation processes [12] (Additional file 1: Section3). Based on reported values [33] 75% nitrogen recovery was assumed in the HTL aqueous phase with nutrients further treated via anaerobic digestion. Overall, the model allowed for 40% of all nutrients to be recycled back to the high-rate ponds [12], where they have previously been reported to support good growth rates [34] (Additional file 1: Appendix 1). CO2 supply was taken from a free issue source (11% CO2 concentration) (Additional file 1: Appendix 1) immediately adjacent to the production facility with all piping, cooling, filtration, and compression accounted for in the cost analysis (Additional file 1: Section3). CO2 was supplied to the algae culture at a concentration of 1% and utilisation efficiency was set to 80% (Additional file 1: Appendix 1). Nutrients were assumed to be non-limiting to growth. A complete description of assumptions and boundary conditions is provided in Roles et al. [12, 29] with advanced components and modifications detailed below and in Additional file 1: Sections13.

Twelve geographical locations were selected across North and South America, Europe, Africa, the Middle East, India, Asia and Oceania (Table 2, Graphical Abstract) for comparative analyses. Sites were selected to cover a broad range of irradiance levels, temperatures and other climatic conditions and economic variables. All sites were chosen, because they provide access to seawater, suitable land and topography (low slope, low density or undeveloped) within a 100km radius.

Under non-limiting nutrient conditions, light and temperature are the most important variables affecting photosynthetic algal growth and the resultant yield of biomass. Light and temperature regimes vary widely between geographical locations and over time due to daily and seasonal cycles. To account for dynamic fluxes in light, temperature and growth, algal biomass productivity was modelled at 1h intervals using typical weather data over 365days of the year for each location. Input variables included: global horizontal radiation (Wm2), diffuse horizontal radiation (Wm2), wind speed (ms1), relative humidity (kgkg1) and air temperature (C, EnergyPlus, US Department of Energy and the National Renewable Energy Laboratory, US). These inputs were used in a heat balance model to predict changes in culture media temperature [35] (Additional file 1: Methods, Section 3). Diffuse and global solar radiation values were used to predict light transfer through the culture [36].

The temperature of the ponds liquid culture was predicted using a simplified mechanical heat balance described by Bechet et al. [35]. Although temperature gradients within the liquid phase can occur, the culture temperature is assumed to be homogenous due to paddlewheel mixing and gas supply. In contrast, the exponential decay of light as it is attenuated by algal pigments through the depth of the culture results in a light gradient ranging from photo-inhibitory light at the pond surface to photo-limited or dark areas toward the pond base. This causes specific growth rates to differ through the culture. Here, we modelled local irradiance along the optical pathlength (i.e., from the pond surface to the base), using a simple and validated radiative transfer model described by Lee et al. [36] that accounts for both direct beam radiation and diffuse, or scattered radiation. Hourly predictions of pond culture temperature, Tpond (t) (C) and local irradiance through the pond depth Iloc (t, z) (molm2s1) were used to predict the specific growth rate of algae using the light and temperature dependent algae growth model described by Bernard and Remond [37]. Growth rates were integrated over time, t, and pond depth, z (m1) to estimate volumetric productivities. Productivity modelling algorithm development and simulations were performed in MATLAB (R2015b, MathWorks).

The full model algorithm is outlined in Additional file 1: Section2. During the growth phase, the volumetric biomass productivity of the system, Pvol (g biomass dry weightL1) was determined by the rate of change of the algal biomass concentration over time:

where Cx is the biomass concentration (g L1), is the specific growth rate (h1) and R is the basal respiration rate (h1). According to Bernard and Remond [37], is a function of irradiance and temperature:

In Eq.2., max is the maximum growth rate of a given species (day1); the light response parameters and Iopt define the irradiance values (mol m2s1) at half saturation rate of photosynthesis (mol m2s1) and at maximum growth, respectively; and is the proportional effect of temperature (dimensionless), using the inflexion function of Rosso et al. [38]:

In Eq.3, the parameters Topt, Tmin and Tmax represent three cardinal temperatures of biological significance, these being, respectively, the optimal temperature at which growth is highest at a given irradiance, and the minimum and maximum temperatures which define the threshold beyond which no growth occurs (Eq.4):

where is the mass extinction coefficient of the algae (m2 kg1, averaged across the 400700nm photosynthetically active radiation range), and is the zenith angle of direct beam radiation hitting the surface of the pond.

where the heat fluxes are solar radiation, Qsolar (W), evaporation, Qevaporation (W), thermal radiation at the pond surface between the air and the water, Qthermal (W) and conduction to the soil, Qconduction (W).

Two industrially relevant marine microalgae species were chosen, Nannochloropsis oceanica and Dunaliella tertiolecta. Both strains exhibit high autotrophic growth rates, a lipid content of ~3040%, and tolerance to wide ranges of temperature and high salinity [41,42,43,44]. This is particularly important for operations under high evaporation conditions which can result in rapid increases in salt content, up to double that of seawater. The growth response parameters to temperature and light (Eq.2) were characterized and validated by Bernard and Remond [37], providing the coefficients listed in Table 1. N. oceanica exhibits optimal growth at a lower optimal temperature and light intensity compared to D. Tertiolecta, suggesting that these species will perform better under temperate and tropical conditions, respectively. For each location, productivity simulations were performed for each strain. The alga exhibiting the highest productivity at each location under the range of conditions analyzed was used for the results reports (Fig.2, Table 2).

The three models used to estimate productivity (liquid culture temperature; local irradiance; and light- and temperature-dependent algal growth) have been previously validated within acceptable ranges against experimental data sets. Lee et al. [36] showed that the simple two-flux approximation predicted local irradiance in a photobioreactor with a variation of 213% compared to more complex radiative transfer models, depending on the time of the day. To ensure the accuracy of our model algorithm, we validated radiative transfer with their reported modelled predictions. The simple radiative transfer equation has been widely used within the literature to estimate light mediated growth. Moreover, Lee et al. [36] found that such differences in estimated PAR resulted in productivity estimations within a 210% variation.

For prediction of temperature of the algal culture, Bechet et al. [35] validated the heat transfer model (Eq.8) with an accuracy of 2.4C against experimental data collected over a 28-day period consisting of 108 temperature measurements taken from the liquid culture of an outdoor 50 L column photobioreactor in Singapore. Because of the complexity of the various heat components of the model, we compared our model simulations against experimental temperature measurements taken within the culture of two 2000 L ponds at the Centre for Solar Biotechnology Pilot plant, Brisbane (Additional file 1: Section 2). The model produced a tight fit between the measured and predicted media temperature in both ponds over a 6-day period, (R20.9).

Beside strain selection, simulations were performed for variables of pond depth (0.10.3m) and quasi-steady-state operating biomass concentrations (0.051gBDWL1). The former affects thermal mass and light regime and latter effects light regime (heat dissipation from algae is considered negligible). Algal productivity modelling algorithm development and simulations were performed in MATLAB (R2015b, MathWorks). All productivity simulations were exported from MATLAB as tables into the TELCA model to optimize harvest regime, depth and concentration to MDSP.

Under a for-profit business model (Scenario 1), the economic effectiveness of algae diesel was assessed using the Internal Rate of Return (IRR) over the life of the facility at a fixed product price. Here, IRR is calculated for each location based on a hypothetical fixed Minimum Diesel Selling Price (MDSP) of US$ 2.00 L1. For Scenario 2, the feasibility was assessed on the cost-competitiveness of the MDSP that could be achieved. In this not-for-profit public utility scenario, profit and tax rates were reduced to zero. Interest rates were reduced from commercial rates to match government bond rates prevailing at each location (Table 2). The resulting MDSP was benchmarked between locations and against existing fossil fuel prices.

Optimization was performed at each location to minimize the MDSP for the following variables: algal strain selection (based on the highest annual-averaged productivity); freshwater replenishment for evaporation (MDSP minimized based on CapEx (e.g., piping, storage) and OpEx (e.g., water purchase, blowdown) requirements over the 30-year lifespan of the facility); operational algae concentration; and harvest duration (MDSP minimized based on CapEx and OpEx over the 30-year lifespan of the facility).

High-rate pond depth and concentration The pond depth, harvest duration and steady-state biomass concentration were the primary set of optimized variables adjusted monthly to optimize MDSP for the entire production, harvest and product processing system. A fixed, rather than variable, harvesting rate was set for the operation as the extra cost for variable speed equipment could not be economically justified. Daily harvest duration was adjusted to optimize culture density for MDSP. In all cases optimum MDSP was obtained by minimizing pond depth. This optimisation, however, was limited to a minimum of 0.25m by engineering constraints of the capacity to construct and operate very shallow depth ponds in conjunction with large 4.3ha pond areas (see Additional file 1: Appendix 1).

Water replenishment Three options to balance evaporative losses after accounting for available rainwater were analyzed (Additional file 1: Section1). Essentially, incorporation of water storage based on a percentage of total pond capacity, replacement with locally purchased fresh water, or replenishment with seawater which necessitates further discharge of pond water (blowdown) to avoid excess salt build-up was analyzed. Blowdown also results in the loss of valuable nutrients from the system. The ideal replenishment choice was location dependent and detailed in the results, but in each case was optimised based on the MDSP.

Labour rates were based predominantly on the tradingeconomics.com/labour-costs website. Rates were established for skilled labour and relative rates were then found for a range of labour categories (Table 2). The categories were identified and estimated for all construction and operational tasks. Base working hours, overtime loadings and non-wage costs were established from various sources (Additional file 1: Section 1).

Labour efficiency was primarily based on GDP per hour worked data, provided in World Bank and OECD databases (Additional file 1: Section 1). GDP output per hour worked for the construction industry differs from these numbers but construction specific and consistent data was only available for European Union countries. For labour efficiency (Table 2), the ratio between whole of economy and the construction sector from the Euro zone countries was assumed to be similar in all countries and was thus used for modelling.

Employment and labour costs The 500ha microalgae facility simulation is based on a set of interconnected process modules (Fig.1b). Each process module accounts for the associated construction and operation tasks. The work required (and associated cost) to complete each task is calculated based on a fixed labour component and process variable labour component. The component variable base was selected based on the most applicable process variable to each task (e.g., Pond Area for pond cleaning, Flow Rate for filter cleaning, Additional file 1: Appendix 1).

Project finance rates applicable to a variety of project types, conditions and risk profiles is generally regarded by industry participants as commercial in confidence. The project interest rates modelled here (Table 2) represent the rates applicable to well established technology being operated by a financially sound project proponent. To provide a broad approach for determining these rates a relationship between government benchmark interest rates and project finance rates was established from known data in the solar PV industry[42].

The costs of supplied construction materials were divided into two groups; fabricated items the price of which was determined in accordance with local labour costs and efficiencies, and equipment supply that would be purchased at internationally competitive rates [29].

The Algae Productivity Model computed hourly growth rates as a function of solar irradiance and culture temperature, based on actual weather data. Simulations were performed over pond depths of 0.10.3m and operating quasi-steady-state biomass concentrations ranging from 0.05 to 1g L1 at each location (Additional file 1: Figures S417). Figure2a summarizes the simulated maximum (11.329.9gm2day1) and average (8.622.1gm2day1) productivities of the best performing algal species at each location. The annual-average range (8.622.1gm2day1) biomass corresponds to 31.480.7Tha1year1. For most locations, higher biomass productivities could be achieved at a greater depth of 0.3m, as this provided more stable temperatures and reduced extreme fluctuations, but only under more dilute operational concentrations (0.1gL1). However, the economic optimization identified a 0.25m depth and a higher operating concentration to reduce harvesting costs (see below). As expected, several near-equatorial locations (e.g., Mombasa, Kenya; Recife, Brazil; Acapulco, Mexico; Darwin, Australia; and Kona, USA) exhibiting relatively high irradiance and air temperature yielded the highest annual-average productivities between 20.0 and 22.1gm2day1. These values are within the range of achievable biomass yields in high-rate pond systems [25, 30]. Abu Dhabi (United Arab EmiratesUAE), also in this cluster, had lower average yields (17.5gm2day1) due to its desert climate of extreme temperatures and irradiance. A second cluster is shown for the sub-tropical locations of Tunis, Tunisia; Almeria, Spain; and Izmir, Turkey at ~1316gm2day1. Haikou (China), with its high temperature but relatively lower irradiance due to relatively high rainfall yielded 11.5gm2day1 and the cool, temperate climate of Amsterdam (Netherlands) yielded the lowest at 8.6gm2day1. The alga D. tertiolecta (Fig.3a, orange circles) performed best in equatorial regions that had consistently high temperatures, while N. oceanica (Fig.2a, blue circles) performed better in locations with cooler climates and lower irradiance. Locations that had a broader temperature range over the year exhibited reduced productivities compared with less variable locations (e.g., Kona, USA and Recife, Brazil) using a single strain (Fig.2a). For example, in the desert climate of Abu Dhabi (UAE), N. oculata exhibited higher productivity through winter (DecMarch), while D. tertiolecta performed significantly better in the summer (MarNov) (Fig.2b). For the technoeconomic evaluation (Fig.4), average productivity values were used for the highest yielding strain (i.e., D. tertiolecta or N. oceanica) for each location to ensure a conservative modelling position. These results indicate that for certain locations, different strains could be used seasonally to improve yields.

a Maximum and average location specific productivities as a function of solar irradiance and temperature for the best performing strain. Maximum productivity refers to the biomass productivity optimized for yield (Light blue: N. oculate; Light orange: D. tertiolecta) at 0.3m pond depth and optimal operating biomass concentration (0.1gL1). Average productivity refers to the biomass productivity optimized for IRR in the techno-economic evaluation (Dark blue: N. oculate; Dark orange: D. tertiolecta) to ensure a conservative techno-economic modelling position (8.622.1gm2day1; see also Fig.1 and Table 3) AB Abu Dhabi, United Arab Emirates; AC Acapulco, Mexico; AM Amsterdam, Netherlands; AL Almeria, Spain; CH Chennai, India; DA Darwin, Australia; HA Haikou, China; IZ Izmir, Turkey; KO Kona, USA; MO Mombasa, Kenya; RE Recife, Brazil; TU Tunis, Tunisia; The light to dark grey shaded circles represent high to low annual irradiance levels. b Strain specific productivity in Abu Dhabi illustrates the benefit of dual strain cultivation over an annual cycle

Breakdown of the key components contributing to the minimum diesel selling price (MDSP). a Scenario 1 (for profit model) shows that 10 locations are profitable at an MDSP of US$ 2 L1. b Scenario 2 (public utility model, not for profit) includes production system costs (blue), land and misc. (yellow), and low interest (red) with 0% tax and 0% profit. Under this scenario, 8 locations (Mombasa, Kenya, Recife, Brazil; Tunis, Tunisia; Acapulco, Mexico; Darwin, Australia; Kona, USA; Chennai, India and Almeria, Spain) could achieve an MDSP of

Late afternoon semi-continuous harvesting is considered optimal for productivity, as it minimises biomass loss via respiration in the dark [43]. While this principle is correct, financially optimised systems (Fig.3, Peak IRR) require the minimisation of harvesting CAPEX (i.e., the smallest harvesting system operated for the maximum duration). In addition, the lowest cost system involves fixed rate harvesting and so can only be adjusted through the start time and operational duration. At an industrial scale, harvesting is usually conducted continuously, to minimise the CapEx of the harvesting equipment (i.e., longer harvesting times=smaller harvest system requirements), with the proviso that this results in a net improvement in the MDSP. The TELCA model has been constructed to conduct such cost benefit analysis and to determine the optimum harvesting regime.We, therefore, focused on minimising the non-harvesting periods, to keep harvesting CapEx low. For all months and at all locations stopping harvest in the morning was found to be beneficial as it allowed cell numbers to increase rapidly during the morning, while harvesting from the afternoon onwards allowed harvesting at higher cell densities, making the process more efficient. Collectively, this high culture density/low harvesting CapEx strategy, yielded a better MDSP and IRR (Fig.4, Table 3). This financially optimised system (Darwin, Australia) increased IRR from~4% (at the peak productivity setting, ~0.1g biomass dwL1 operating concentration) to 13% (at a 3.5 fold higher operating concentration, ~0.35g biomass dwL1).

Productivity increased with pond depth (gm2) in most locations, but IRR decreased. The use of shallower ponds (i.e., 25 vs. 30cm) with higher concentration reduced harvesting costs. Construction and thermal stability constraints for large shallow ponds limited further depth reductions.

Saltwater systems are designed to minimise their environmental freshwater-use footprint. Ideally, evaporated water is replenished with rainwater but in practice an imbalance usually exists and must be corrected. Three options to balance evaporative losses after accounting for available rainwater were analyzed: (1) incorporation of water storage based on a percentage of total pond capacity; (2) replacement with locally purchased fresh water; or (3) replenishment with seawater which necessitates further discharge of pond water (blowdown) to avoid excess salt build-up, but also results in the loss of valuable nutrients from the system. For all locations, the option of water storage between rain events proved to be the least economic due to added CapEx and land requirements, and consequently required a combination of freshwater and seawater replenishment. For financial optimisation, the proportion of freshwater purchased vs. new seawater added with blowdown to maintain salinity was location dependent, ranging from 0% freshwater purchase in Abu Dhabi (UAE), where the cost of freshwater is high (US$ 0.86 kL1), to 96% in Chennai (India), where the freshwater price is low (US$ 0.04 kL1) (see Table 2). Consequently, the high discharge rates in Abu Dhabi resulted in an approximate twofold increase in nutrient costs compared to most of the other locations analyzed (Fig.4a).

Hydrothermal liquefaction-based biorefinery methods have shown conversion rates of total biomass to oil at an efficiency of 55% [31, 32] which is significantly higher than processes based on traditional oil extraction (e.g., Tri-acyl glyceride extraction). Based on the simulated average biomass productivities (8.6gm2day1 to 22.1gm2day1, see Fig.2a, Table 3) and downstream processing, this equates to biodiesel yields ranging from 17 kL ha1 year1 (Amsterdam, Netherlands) to 44.7 kL ha1 year1 (Mombasa, Kenya). A summary of reported oil yields among 20 studies [44], showed estimations of algal-based biofuel ranging from 10kL up to 130kL ha1year1; however, the vast majority of studies ranged from 15 to 60kL, suggesting that our indications are in the mid-range of previous reports.

Under a commercial for-profit business model scenario (Fig.4a), the IRR is calculated for the described algae biodiesel production system at each of the 12 chosen locations on the basis of a US$ 2 L1 MDSP (Graphical abstract, Table 3). This enables the identification of specific cost components that can be further optimized to drive the MDSP down towards cost parity with fossil-fuel-based diesel. At all locations, the proportional contribution (US$ L1) rather than the absolute cost (US$ system1) of each component is shown. To agglomerate construction and operational costs, all future costs were discounted at inflation-adjusted interest rates prevailing in each jurisdiction (Table 2). IRR values are presented for a full for-profit business model (at an assumed US$ 2 L1 Minimum Diesel Selling Price (MDSP) at the factory gate) and range from 7% (Amsterdam, Netherlands) to close to 22.1% (Mombasa, Kenya), with four locations presenting an IRR>18% (Table 3).

The not-for-profit public utility model excludes the need to generate profit and assumed 0% IRR, 0% tax and a base interest rate aligned to respective government bond rates. The achievable Minimum Selling Price and component breakdowns are shown in Fig.4b. The achievable MDSP under a public utility model ranged from US$ 1.152.61 L1 (US$ 4.359.87 gal1): i.e., Chennai, India and Amsterdam, Netherlands.

Labor cost in terms of the final MDSP ranged from 6.8% in Chennai to 38% in Amsterdam (Fig.4a). It includes direct-wage costs, non-wage on-costs and labor productivity in each location (Table 2). Process automation could significantly impact the final product price.

Equipment supply and operational supply costs were reasonably consistent (2133% of MDSP) across all jurisdictions (Fig.4a), being based on international supply prices. The differences were predominantly due to different production rates and applicable discount rates (Table 2). It is not anticipated that major savings can be made here but incremental improvements are possible.

Fabrication (48% of MDSP) was modelled to occur in each local jurisdiction, except the Netherlands, Spain, USA and Australia (Fig.4a). Importation of fabricated items (e.g., steelwork) into these excepted countries from lower cost centers resulted in minor real cost differences across the range. Notably, high discount rates reduce the contribution of future costs and consequently increase the impact of fabrication costs associated with upfront construction. In some countries (e.g., Turkey, Brazil and Kenya) this effect had a notable impact. Consequently, it is anticipated that any savings in fabrication will have little impact on MDSP.

Nutrient costs (29% of MDSP) are generally directly proportional to the biomass production in each location (Fig.4a). The major exception to this was Abu Dhabi, where high evaporation rates required increased saline discharge resulting in high nutrient losses. The use of strains able to grow in hyper saline conditions can help to reduce nutrient losses and freshwater costs as discharge between rain events can potentially be reduced.

Land costs were a comparatively small contributor to overall MDSP and viability (Fig.4a) but ranged from 0.2% in Tunisia and Australia, to 6.8% in Turkey (influenced by the discount rate) and 7.2% in the Netherlands. These figures represent the current value of suitable land. Ultimately microalgae system deployment, however, may be affected by the absolute land availability in some locations, such as Europe, USA and China. It is anticipated that significant savings in land costs are unlikely.

CapEx and Opex: the CapEx for the 500ha facility (Fig.4c) ranged between US$ 128245 Million (i.e., US$ 256,000490,000ha1), with the three largest cost components being growth, HTL/refining and harvest/concentration systems. On a per hectare basis the sub-component CapEx cost of the algae production portion for the 500ha facility (Darwin, Australia) was US$ 446,000ha1. This is ~15% below the actual construction cost of a 0.4ha facility (US$ 525,000). This cost saving is in line with expected economies of scale achieved through the scale up from 0.4 to 500ha (1250-fold scale up). The annual OpEx (Fig.4d) ranged between US$ 7.716.4 Million (i.e., US$ 15,40032,800ha1) with the three largest cost components being growth, harvest and utilities.

However, CapEx and OpEx alone were insufficient to predict profitability. For example, despite Darwin and Amsterdam having similarly high CapEx values (~US$ 223 and US$ 245 Mil, respectively) and OpEx (US$ 13.7 Mil year1 and US$ 12.6 Mil year1), due to its operational conditions Darwin yielded an IRR of 13.2% (vs. Amsterdam 7%) and an MDSP of US$ 1.23 (vs. Amsterdam US$ 2.61) in the not-for-profit scenario. This highlights the importance of local climatic and operational conditions.

Employment: the facility will typically employ around 290 personnel during 2years of design and construction and 74 personnel on a continuous basis during the 30-year operational life of the plant (based on Darwin, Australia).

Local policy settings also have a major impact. For example, a public utility approach can considerably reduce the price of fuel providing a key step on the path to a profitable commercial renewable fuel industry by attracting the required investment needed to advance technology and commercial biorefinery co-production options.

Interest rates were the second largest factor effecting financial viability. Their effect on the MDSP varied between 5% in Spain and 44% in Turkey (Fig.4a). The 24% project finance rate prevailing in Turkey during 2018 when this data was assembled was the primary reason that this location did not return a positive IRR.

Profit: the most profitable location was Almeria primarily due to its very low discount rates and despite its modest 10.9% IRR. At the set US$ 2 MDSP price, most of the locations (Fig.4a) demonstrated surprisingly similar inflation adjusted profits. Haikou, China had a comparatively low 9.5% IRR further reduced by the 6.5% discount rate. This lower IRR may stem from the particular climatic conditions in the selected location of Haikou, whereas other locations within China may deliver better results. The least profitable locations were Izmir (Turkey) and Amsterdam (Netherlands). While interest rates were the primary negative factor for Turkey, the Netherlands was affected by a combination of high labor and land costs and the lowest biomass productivity (8.6gm2day1). This information suggests that Turkey would be more attractive for renewable fuel production under conditions of reduced sovereign risk; Amsterdam appears better suited to the expansion of microalgae industries focused on higher value products.

The CO2-eq emissions of microalgae diesel correspond to about one-third of non-renewable diesel based on the boundary conditions set in this study and so process profitability would benefit from carbon pricing. Carbon pricing was (2018) only applicable in 4 of the 12 locations, Amsterdam, Netherlands (US$ 8.20T1 CO2 equivalent greenhouse gas emissionsCO2eq), (Almeria, Spain (US$ 8.20T1 CO2eq), Chennai, India (US$ 5.85T1 CO2eq) and Acapulco, Mexico (US$ 3.50T1 CO2eq). Nevertheless, the locations governed by a carbon price, and the price itself are forecast to rise in the coming years. The Carbon Pricing Leadership Coalition forecasts that a carbon price of US$ 100T1 by 2030 [45] will be needed as one of a series of measures to stay within a 2C rise in global temperatures. The effect of carbon pricing was, therefore, also analyzed at US$ 100T1 for Almeria (Fig.4a) to measure its effects at this forecast future price point. Under a US$ 100T1 carbon price the profitability rose from 10.9 to 14.1%.

A rapidly expanding body of advanced climate [46] and global energy-use data [7] has firmly established the urgent need for strategic leadership and action on CO2 emissions reductions. Failure to deliver this is forecast to influence the future for centuries, if not millennia [46]. Despite significant advances in renewable stationary energy and electric vehicles, parallel development of renewable fuels (e.g., methane, ethanol, high-density liquid fuels and H2) is critical to meet international COP21 Paris CO2 emission reduction commitments and key UN Sustainable Development Goals (in particular SDG 7: affordable and clean energy and SDG 13: climate action, and others indirectly) [47]. Microalgae-based renewable fuel systems are a frontrunner option that can help to support this energy mix as they can supply high-density liquid fuels for aviation, shipping and long-haul transport using existing infrastructure with relatively low environmental impact. Moreover, the vulnerability of global supply chain disruptions revealed during the COVID-19 crisis underscores the importance of decentralized and distributed energy networks consistent with algae-based fuel production.

Prices of non-renewable diesel over the past 20years have ranged between US$ 0.191.04 L1. For the purposes of this study, we have set a benchmark target for microalgae diesel to achieve price parity, to US$ 0.80 L1 (US$ 3.02 gal1). It should, however, be noted that in 2019 the International Monetary Fund (IMF) concluded that fossil fuel subsidies, defined as fuel consumption times the gap between existing and efficient prices (i.e., prices warranted by supply costs, environmental costs, and revenue considerations), for 191 countries ranged between US$ 4.7 (2015)5.2 trillion (2017), corresponding to 6.36.5% of annual GDP, respectively. Furthermore, the IMF concluded that Efficient fossil fuel pricing in 2015 would have lowered global carbon emissions by 28 percent and fossil fuel air pollution deaths by 46 percent, and increased government revenue by 3.8 percent of GDP [48]. These factors are likely to exert an upward pressure on the price of traditional non-renewable diesel into the future. In contrast, technical and policy advances foresee a downward trajectory of microalgae renewable fuel price toward an intersect in the price of non-renewable dieselespecially if meaningful carbon pricing can be implemented.

In terms of technical advances, Fig.2b, shows that the use of the dual microalgae strain approach, designed to optimize biomass productivity over the full 12-month period can increase the IRR in Abu Dhabi from 11.5 to 14.4%. Recent advanced, synthetic cell engineering technologies have potential to greatly improve algae traits for increased photosynthetic efficiency, biomass and lipid yields. For instance, ExxonMobil (EM) has partnered with Synthetic Genomics, Inc (SGI) with the aim to produce 10,000 barrels of algae fuels per day by 2025 [49]. Using synthetic biology techniques, EM-SGI researchers doubled lipid content of Nannochloropis gaditana by fine tuning a genetic switch that partitions carbon to oil, without compromising growth [50].

Automated pond construction techniques and process automation are likely to reduce CapEx and OpEx. Atmospheric CO2 capture [51], optimisation of light capture [40, 52, 53], production conditions [54, 55], strain selection [56] and breeding [57, 58] can also increase productivity. Improved biomass productivity can also reduce harvesting costs due to increased cell densities (Fig.3). Bio-refinery concepts for the co-production of fuel and other higher value co-products can also improve profitability (see below).

Our analyses show that under a for-profit business model focused only on diesel production, 10 of the 12 locations achieved a minimum diesel selling price (MDSP) under US$ 1.85 L1/US$ 6.99 gal1 and nine under US$ 1.60 L1 (US$ 6.04 gal1). While encouraging, US$ 1.60 L1 is still US$ 0.80 L1 above the non-renewable diesel benchmark price of US$ 0.80 L1. Increased international carbon pricing could reduce this gap but has proven difficult and consequently this study highlights an alternative path to competitive low CO2 emissions renewable fuel systems [29].

Under the not-for-profit utility model, eight locations achieved an MDSP of less than US$ 1.25 (US$ 4.73 gal1). This price comparison can be extended to most other fuel types (e.g., jet fuel, petrol and bunker fuel), as the production and processing costs are similar on an energy content basis. The establishment of fuel utilities could, therefore, bring microalgae fuel prices to within US$ 0.45 L1 of the US$ 0.80 non-renewable diesel bench mark price and less in an environment in which fossil fuel subsidies are reduced. Chennai actually returned an MDSP of US$ 1.15 reducing this gap to US$ 0.35 L1. While a fuel price of US$ 1.151.25 is still US$ 0.350.45 L1 above the US$ 0.80 non-renewable diesel benchmark, the introduction of co-product streams (e.g., protein, biopolymers and nanomaterials) could bridge this gap on the path to fully commercial biorefineries under future policy setting in which the carbon-price increases over time.

Microalgae-based fuels also offer local benefits through the provision of employment. For example, the Darwin (Australia) facility would employ 290 personnel during 2years of design and construction and 74 personnel on a continuous basis during the 30-year operational life of a 500ha plant. It would also support sustainable economic development which in turn can generate tax income [12]. Internationally, these technologies could provide a series of advantages which range from economic resilience and increased fuel, climate, political, social and environmental security enshrined in the UN Sustainable Development Goals (in particular Affordable and Clean Energy and Climate Action). Microalgae also provide mechanisms to contribute to circular economies and to support initiatives to keep these within our planetary boundaries.

The CapEx for the twelve 500ha facilities simulated (Fig.4c) ranged between US$ 128245 Million (i.e., US$ 256,000490,000ha1) and the annual OpEx (Fig.4d) between US$ 7.716.4 Million (i.e., US$ 15,40032,800ha1). CapEx and OpEx alone were insufficient to predict profitability as climatic, operational and economic conditions had major impacts (Figs. 1, 3 and 4) highlighting the importance of conducting the location specific analyses presented. Under a for-profit business model focused only on diesel production, 10 of the 12 locations achieved a minimum diesel selling price (MDSP) under US$ 1.85 L1/US$ 6.99 gal1, while using the not-for-profit utility model, eight locations achieved an MDSP of less than US$ 1.25 (US$ 4.73 gal1). Moving forward, the judicious use of technology and policy optimisation could help to bridge the gap on the path to fully commercial biorefineries under future policy setting in which the carbon-price increases over time. The TELCA model can now be used to enable model guided systems design, assist with systems optimization, de-risk scale up and advance business models. The analysis presented also provides governments and other investors with a solid basis on which to assess whether they wish to encourage establishment of a microalgae industry in their jurisdiction, and if so, which technical advances and policy settings are likely to be most favorable. The analysis indicates that microalgae high-density renewable liquid fuels could be produced close to competitively in a broad range of countries (Graphical abstract and Fig.4) and that price parity is likely achievable through the introduction of scaleable and higher value co-product streams (e.g., protein and biopolymers). As has been demonstrated in numerous other industries, early adopters are likely to be best positioned to establish the critical mass necessary to develop beneficial value chains, supply local markets and expand export opportunities.

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The authors gratefully acknowledge the support of the Australian Research Council (LP150101147, DP150100740 and LP180100269) and the Science and Industry Endowment Fund (John Stocker Postdoctoral Fellowship PF16-087).

Authors made the following contributions to this paper: JRsoftware development, economic modelling, validation, paper writing. JYsoftware development, light modelling, validation, paper writing. KHpolicy guidance. BHconcept, paper writing. All authors read and approved the final manuscript.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Roles, J., Yarnold, J., Hussey, K. et al. Techno-economic evaluation of microalgae high-density liquid fuel production at 12 international locations. Biotechnol Biofuels 14, 133 (2021). https://doi.org/10.1186/s13068-021-01972-4

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