These seemingly useless concrete blocks mixed with pavement cement blocks, waste, waste rock, and garbage are only to be sorted and recycled, and then can completely "turn waste into treasure" and realize its economic value.
The traditional treatment method only involves the physical migration of concrete construction waste, which will bring a safety hazard, result in huge pollution of water resources, land resources and air resources, and occupy a large amount of land area.
1. Production of coarse and fine aggregates: Combined with the strength grade of construction waste, concrete aggregates with different hardness and grain size can be processed, and can also be used for road pavement base filler.
2. The production of new, environmentally friendly building materials: Recycled concrete construction waste and cement blocks are often used to manufacture environmentally-friendly bricks, insulation materials, insulation walls around the outside of the wall, dry mortar, etc.
4. Temporary road laying: A large number of cushions are required in the road construction process, so the concrete construction waste can play a huge role in the laying of road cushions and temporary roads.
Also, concrete waste is always mixed with scrap wood, which can be used for wood regeneration. For the waste wood in construction waste, the well-preserved can be directly used for reconstruction, and the damaged materials can be raw materials of regenerating wood, or used in the paper-making field.
The specific use of crushed concrete waste also depends on the processing effect of the selected equipment. At present, there are two kinds of crushing production lines for processing concrete waste, one is fixed concrete crushing production line, and the other is mobile crushing production line.
The fixed type is suitable for some stone factories and concrete wastes need to be equipped with professional crushers, iron removal equipment, professional dust removal and noise reduction equipment, vibration feeders, magnetic separators, flotation machines, vibrating screens, belts, etc., which forms a complete production line for concrete waste treatment.
The mobile type is more suitable for housing developers and concrete waste disposal companies. It is easy to transport and can be pulled into the construction site for quick operation, which saves the space without having to build a special factory.
It can produce different aggregates with various specifications, achieving the needs of different customers. This kind of mobile construction waste treatment equipment is widely used, and its main features are as follows:
The most commonly used crushing machines are jaw crushers, hammer crushers, and impact crushers. Which one to choose, users need to make choices according to their own needs and actual conditions, and the most important thing is to make choices according to the characteristics of the crushers.
Uses: The impact crusher can handle the crushing of various coarse, medium and fine materials with a length of less than 500 mm and a compressive strength of less than 350 MPa. The discharging granularity can be adjusted as needed to adjust the gap between the hammerhead and the counterattack plate.
Uses: Jaw crusher is a piece of early crushing equipment. Because of its simple structure, firmness, reliable work, easy maintenance and overhaul, and relatively low production and construction costs, it is still widely used in metallurgy, chemical industry, building materials, etc.
Concrete waste is made up of chemical constituents of silicates, oxides, hydroxides, carbonates, sulfides and sulfates, which means that it is soft and has a certain strength, hardness, impact and wear resistance.
1. The impact crusher can effectively treat materials with large moisture content and prevent clogging of the crusher. Therefore, when the concrete has a large moisture content, the impact crusher is the best choice.
2. The hammer crusher has less investment, large output and low power consumption. When the user has a large demand for production and considers the investment problem, hammer crusher is the best choice.
3. The jaw crusher is widely used, especially for one-stage crushing, which is to crush large materials into two-stage crushing machines. If the feeding materials are large, it must be crushed by jaw crusher.
There are many manufacturers of concrete waste crushers. Most of the famous brand concrete crusher manufacturers provide perfect one-stop service before-, during- and after-sales, which can save the cost of manufacturers, so the price of the concrete crusher will be lower.
Fote Heavy Machinery is a professional crusher manufacturer integrating R&D, production and sales. 37 years of production experience has established Fote's leading position in the crushing industry. The crusher produced by Fote is economical and affordable, which is the best choice for users.
And Fote adopts the self-produced and self-selling development model, which can directly contact and communicate with customers so that it can effectively carry out targeted equipment process improvement and model adjustment according to the production needs of customers, tailoring the equipment for customers.
As a leading mining machinery manufacturer and exporter in China, we are always here to provide you with high quality products and better services. Welcome to contact us through one of the following ways or visit our company and factories.
Based on the high quality and complete after-sales service, our products have been exported to more than 120 countries and regions. Fote Machinery has been the choice of more than 200,000 customers.
Construction waste is usually processed with two types: mobile crushing plant and stationary crushing plant. Factory regeneration use the stationary crushing equipment to process the construction waste in the factory.
1. Technological Process: Construction waste ----- transported by the dump truck ----- Vibrating feeder ----- Jaw crusher (Primary crusher) ----- Impact crusher (Secondary crusher) ----- Circular vibrating screen ----- finished product as the customer required 2. Max feeding size: 1000mm 3. Output size: As the clients required (1-50mm) 4. Application: sand& aggregate industry, recycled concrete aggregate, new type raw materials for wall, and filling accessories for road base course.
DSMAC has always pursued the service concept of "Create customer value, the customer is always right". As for service, clients' needs have always been our primary concern. Through standardized, differentiated and super valued service, we can reduce clients' psychological cost and use-cost, and ultimately increase clients' transition value, profitability and purchasing power. As a result, we can improve DSMAC's service brand competitiveness and lead the way of service for fellow competitors.
Service Network Presently, DSMAC has offices and branch companies in more than 10 countries and regions, and 31 offices in China. In addition to traditional after-sale service, our company offers internet sales and product trace services. We guarantee that we will offer our valued clients timely and thorough services.
Service Team Our company has 30 engineers providing professional after-sale services. They are all skilled, experienced and familiar with the working principles of various machines and equipment. We promise that we will arrive on scene within 48 hours for 1000 kilometers when we get a call from our clients and not over 72 hours for clients farther away.
Limestone, also known as calcium carbonate, is a compound that is a common substance on earth and can be found in rocks. It exists in nature as calcite and aragonite. In the physical properties of limestone, the crystal size of calcite is very important. The limestone crushing production line is an important production link in the lime burning production line and the cement production line. The material is conveyed to the jaw crusher by the vibrating feeder. After the material is crushed, it is sent to the impact crusher or the cone crusher by a belt conveyor for crushing. After the crushing, the material enters the vibrating screen for screening, and the finished material is sent by the belt conveyor. Unqualified materials are transported back to the impact crusher or cone crusher for crushing again. Limestone can be used as a building material in large quantities, and it is also an important raw material for many industries. Limestone can be directly processed into stone and burned into quick lime. Lime includes quick lime and hydrated lime. Quicklime becomes slaked lime by absorbing moisture or adding water. Slaked lime is formulated into lime mortar, lime paste, lime mortar, etc., which is used as coating material and tile adhesive. Quicklime is used as a desiccant and disinfectant. In agriculture, quicklime is used to prepare lime sulfur mixture, Bordeaux mixture and other pesticides. The application of hydrated lime in the soil can neutralize the acidity of the soil, improve the structure of the soil, and supply the calcium required by the plants. Brushing the trunk with lime mortar can protect the trees. According to the customer's production needs, we can develop a better, more cost-effective selection program, and give customers scientific and reasonable prices for your limestone processing project to create more market profits. If you are interested in the limestone crushing production line, you can click on the online customer service for a detailed consultation.
DSMAC successively undertook lots of large engineering projects, such as Tianrui 8000 TPD tailings crushing production line, Jinlong 8000 TPD sand and gravel crushing joint production, and Changjiu 20000 TPD construction aggregate crushing plant. DSMAC has had more than 10 years experience of design, configuration and installation; currently it has the ability of turnkey general contracting service for 1000-20000TPD gravel and aggregate crushing production line project.
Aggregate crushing line has been widely used in building, road and railway construction. According to the production, it can be divided into 50-80T/H, 80-120T/H, 120-150T/H, 150-200t/h, 200-250t/h, 250-350t/h, etc. DSMAC can offer both of stationary and mobile construction aggregate crushing plant. Are you interesting? We are always waiting for you!
The stone crushing plant is a kind of special plant for building stone production. AGICO stone crushing production line is a project launched by our company after several years of development and research. Compared with the traditional crushing model, it saves a lot of energy and labor. At the same time, it has the characteristics of reasonable design, reliable operation, convenient operation, high efficiency, and energy-saving. We provide the equipment and design the production line according to the customers specific needs. AGICO has the ability to provide you with the best technical support.
The stone crushing plant manufactured by AGICO mainly consists of a vibrating feeder, jaw crusher, impact crusher, vibrating screen, belt conveyorcentralized electronic control system, and other equipment. The designed output is generally 30-350 tons per hour. According to different technological requirements, we can also equip the dust collector, cone crusher, or other cement crushersto meet the different production needs of customers.
In the production process, large stones are evenly sent to the jaw crusher by a vibrating feeder for coarse crushing (primary crushing), then sent to the impact crusher by the belt conveyor for secondary crushing. After that, all the stones will be divided into three different sizes on the vibrating screen. The large-sized stone which beyond the specification will be sent back to the impact crusher by the belt conveyor for re-crushing, and then sent to the vibrating screen again forming closed-circuit multiple cycles until the completion of production.
Our stone crushing plant has a high degree of automation. Except for equipment start-up, shutdown, and daily maintenance, the whole plant requires almost no manual operation. Besides, our equipment is easy to maintain. The wearing parts are made of high-strength wear-resistant materials, with a small loss, and long service life, which can bring considerable economic benefits to customers.
This plant has been successfully applied in the crushing processing of limestone, basalt, granite, pebble, and other rocks. The product quality completely reaches the GB14685-2001 standard, which can be regarded as aggregate for high-grade highway, railway, water conservancy, concrete mixing plant, and other industries.
The performance of the stone crushing plant can be judged according to the product quality. There are mainly two quality indexes of crushed stone: the ratio of elongated and flaky particles and the content of the powder. If these two indicators exceed the specified range, the product quality is unqualified and cannot be used in large projects. The quality of crushed stones produced by our crushing plant is in line with the relevant standards. The content of elongated and flaky particles and powder in the crushed stone is very small and the product particle size is uniform, the particle shape is good with no internal crack, the compressive strength is high, and the granular composition is reasonable.
AGICO has large manufacturing workshops and professional processing equipment to ensure the fast and high-quality production of related equipment in the automatic stone crusher plant. In addition, we have more than 20 years of production and sales experience, the products are exported to all over the world, therefore, we can ensure the timely and safe delivery, reducing the time cost of customers.
AGICO provides EPC turnkey projects. It not only includes the manufacture of various specialized equipment in the stone crushing plant but also includes the plant design, onsite installation, real-time commissioning, equipment operation training and usual spare parts service.
Customization is the most basic service our company provides for each customer. We will design the production lines and choose equipment according to customers specific needs on stone specification, output, application, construction environment, etc. Every customer will get their satisfied stone crushing production line here.
Jaw crusher is mainly used for raw material coarse and medium crushing in the cement plant and crushing plant. According to the width of the feed port, it can be divided into three types: large type (feed port is larger than 600mm), the medium type (feed port is between 300-600mm), and small type (feed port is smaller than 300mm). It features small noise, small dust, simple structure, low cost, which is the idol choice for raw material crushing.
Impact crusher can handle the material with side length between 100-500mm and the resistance pressure no higher than 350mpa. It has the advantages of large crushing ratio, low power consumption and long service life. Its discharge size is adjustable, and after crushing, the material presents a cubic shape. As a crushing mill that can finely crush materials, it is widely used in building material, tone crushing, railway, cement, and chemical industry.
AGICO Group is an integrative enterprise group. It is a Chinese company that specialized in manufacturing and exporting cement plants and cement equipment, providing the turnkey project from project design, equipment installation and equipment commissioning to equipment maintenance.
Coke is an important raw material in the steel industry, and its quality directly influences the smelting of iron and steel. To improve coal quality and reduce coal blending costs, we need to predict the coke quality and optimize the coal blending scheme. In this paper, we propose a modeling and optimization method based on the characteristics of the coal blending and coking process. First, we establish a model for predicting coke quality from coking petrography data, based on Gaussian functions and Xgboost-SVR. The model has two components. In the first part, we analyze the key characteristics of the coal blending and coke process, and extract features of the vitrinite reflectance distribution with Gaussian functions. In the second part, we use Xgboost to select a representative feature subset, and then use support vector regression (SVR) to create a model for predicting coal quality. Next, we formulate a multi-constraint optimization problem to describe the coal blending costs, and solve it using a modified particle swarm optimization. Finally, we demonstrate the effectiveness of our modeling and optimization method by applying it to actual process data. This shows that our proposed method can improve prediction performance and reduce the coal blending costs.
Coke is one of the most important raw materials in the steel industry. It can be used to provide heat for melting slag and metal (as fuel), reduce iron ore to elemental iron (as a reduction agent), and maintain permeability in blast furnaces (as a permeable support), , , . With the increasing use of large-scale blast furnaces, high-quality coke is in high demand in the steel industry. To make efficient use of coal resources, improve coke quality, and reduce the coal blending costs, we need to research methods for predicting coke quality and optimizing coal blending methods.
In the coal blending and coking process, different types of coal are transported to a funnel by a stackerCreclaimer and blended in given proportions to form the corresponding blended coal, which is sent to the coal tower. Then, the blended coal is removed from the coal tower, measured, and placed in the coke oven. Finally, the coke is formed by a period of high-temperature carbonization in the coke oven. Fig.1 illustrates the coal blending and coking process.
From Fig.1, we can see that optimization of coal blending costs means we should find a coal blending scheme to minimize the costs generated by coal consumption when the production quality requirements are met. Therefore, from the perspective of optimization problem, the objective is to minimize the costs of raw coal. Meanwhile, the production requirements and quality requirements of coal blending process bring the optimization problem a variety of complex constraints. Among these constraints, the quality of coke produced by coking is a critical parameter, which needs to be accurately predicted. However, the coking process is a complex chemical process, the production condition in the coke oven is difficult to measure, more difficult to inference and calculate. Therefore, its more doable to use various coal quality indicators to propose an efficient and accurate coke quality prediction method.
The final coke quality is, to a certain extent, determined by the coals properties. Blended coal is formed by mixing different types of coal in given proportions, and only one physical change occurs. Consequently, the properties of the blended coal are determined by the properties and proportions of the different types of coal, and these in turn enable us to predict the coke quality. However, the relationship between the different types of coal and the resulting coke quality has three important characteristics
1. Multiple feature types: No one feature can fully reflect coals properties, and different features influence the coal quality in different ways, . Larger amounts of impurities, mainly ash and sulfur, form wider, deeper, and longer cracks in the coke. These reduce both the mechanical strength and strength after reaction (CSR), as well as increasing the coke reactivity index (CRI). Coal rank parameters, such as the amount of volatile matter, reflect the degree to which the coal has metamorphosed. High amounts of volatile matter increase the cokes shrinkage rate, reduce its crushing strength, increase its porosity, make the pore walls thinner, and affect the CRI. Low amounts of volatile matter lead to the coal having poor caking properties and reduce the cokes wear resistance. Coals caking properties, mainly the bonding index, glial index, and glial thickness, reflect its ability to form plastic compounds during the coking process. A sufficiently high bonding index is needed to produce strong coke, but too high a bonding index actually decreases the coke strength. During the coking process, the glial index and glial thickness affect the volatile matter overflow, which affects the crushing strength (M40), wear resistance (M10), and CRI. The vitrinite reflectance, an important factor in coal petrography that mostly reflects the coals microstructure, , , , , , can better reflect the coals level of metamorphism, which ultimately affects the CRI and CSR.
2. Relationships between coal features: Impurities destroy coals caking properties, while a low volatile matter content reduces them. Therefore, the ash, sulfur, and volatile matter contents are all related to the caking indicators. The vitrinite melts when the coal is heated, affecting its caking properties. On the other hand, the volatile matter content is affected by the vitrinite content. Thus, the model must consider the relationships between different feature types.
3. Nonlinearity: The coking process is strongly nonlinear. Coke, gas, and chemical products are formed by drying, pyrolysis, melting, solidification, semi-coking, and shrinkage processes in the coke ovens carbonization chamber. These chemical and physical changes lead to nonlinear relationships between the coal features and coke quality.
The above analysis indicates that, during the coal blending and coking process, different types of features affect the coke quality via different mechanisms, and features of the same type have different effects on the coke quality. In addition, there are complex relationships between the different coal features, making it difficult to select suitable input features for a prediction model and determine its structure.
With regard to coke quality, it is currently predicted using traditional coal indicators, such as impurity levels (ash Ad, sulfur St.d), coal rank parameters (volatile matter Vdaf), and caking properties (bonding index G, glial index X, and glial thickness Y), , . However, these ignore macerals, meaning that they cannot accurately measure the level of metamorphism or the coals caking properties. It is difficult to describe the strongly nonlinear relationships between coal properties and coke quality, but coal petrography, a new area of coke research, can address this issue by analyzing the coking mechanism from a microscopic point of view and revealing the influence of different macerals on the coking process, which helps greatly in predicting coke quality.
Consequently, vitrinite reflectance, a crucial coal petrography index, has been widely researched, revealing that it is strongly correlated with coke quality. Although this correlation can be used to guide coal blending, to some extent, its complexity means that it cannot be used to predict coke quality directly, limiting its applications.
Given this, several mechanisms and models have been proposed for predicting coke quality, such as the Composition Balance Index-Strength Index, Combined Coal Index (CCI), Composite Coking Potential index (CCP). These have, to some extent, overcome the defects of traditional methods, improved the accuracy of coke quality predictions, and opened up new directions and ideas for coal blending. However, they require complete measurements of all macerals, a substantial and cumbersome task, and rely heavily on the results of laboratory experiments. In addition, they neglect the effects of impurities, coal rank parameters, and caking properties on coke quality. Ultimately, the differences between laboratory experiments and actual industrial environments greatly limit their applicability.
These issues led to the development of several data-driven models, such as radial basis function (RBF) neural networks. These combine the vitrinite reflectance with traditional indicators to predict coke quality. However, the vitrinite reflectance curve has a certain physical meaning, and a given coals vitrinite reflectance distribution reflects its coking properties comprehensively and accurately. When the distribution is smoother and more Gaussian, coal blending gives better results, . Unless we consider such distribution characteristics, the resulting coke quality predictions will be unreliable.
As for the optimization problem, several methods have been proposed for optimizing the coal blending scheme using data-driven models. These typically handle the problem by constructing constraint optimization problems and then selecting a suitable optimization algorithm, such as a genetic algorithm (GA) or particle swarm optimization (PSO), to obtain an optimal blending scheme, . GA, differential evolution, PSO, , and other intelligent optimization methods are commonly used to solve industrial optimization problems with low real-time requirements, due to their outstanding ability to handle constraints and nonlinearities.
Besides, lots of EAs have been improved and applied in different engineering optimization. To get over the parameters tuning of search group algorithm (SGA), Noorbin and Alfi  proposed an adaptive parameter control using fuzzy logic, namely fuzzy SGA (FSGA), and its performances on benchmark problems and networked control systems are outstanding. Mousavi and Alfi proposed an fractional-order firefly algorithm, incorporating fractional calculus during the search process, which simulates the behavior of each firefly with historical memory. Many of these give us inspiration to use EAs to optimal coal blending costs.
Comparing with other EAs, its quite appropriate to optimize coal blending costs using PSO. Because it has a character that its ability and robustness to search the optimal solution can be improved significantly by population size. Although the computation time will increase with population size, its still beneficial because this problem is not a real-time control problem. Besides, PSO has outstanding ability to deal with complex constraints brought by the nonlinear coke quality prediction model. Whats more, PSO is easy to realize and improved in engineering practice.
However, the performance of standard PSO greatly depends on the parameter values used and it often becomes trapped in local optima, resulting in premature convergence. Focusing on search capability of particles and balance between exploitation and exploration, lots of improved PSO designed adaptive inertia weight which can adapt to the fitness of particle and the number of iteration , , . However, the optimization of coal blending scheme contains many constraints, which are usually dealt with by penalty function in PSO.Shahri etal.  introduces fractional-order in augmented Lagrange PSO to improve its convergence ability. But it is always difficult to select appropriate penalty factors in PSO . Besides, the coke quality constraints involve complex nonlinear equations. If we want to reduce coal blending costs as far as possible, it will be better to design an algorithm which can improve PSOs convergence ability with an more appropriate strategy to deal with constraints.
1. To establish a reliable model of coke quality, there are several problems needed to be discussed. First, how to property and efficiently utilize vitrinite reflectance whose distribution characteristic is complex? Second, how to select the best features which are highly interrelated that influence most on coke quality? Third, how to clearly describe the nonlinear relationship between the selected coal features and coke quality?
2. Its hard to solve the optimization problem because the equations of coke quality constraints are nonlinear and non-convex if we want to accurately describe this process. Whats more, coal blending process has many other complex input constraints and state constraints, which make it hard to find the optimal coal blending scheme.
Considering all the above issues led us to propose a new data-driven modeling approach that optimizes the coal distribution cost. The model uses Gaussian functions to extract the features of the vitrinite reflectance distribution, which are then combined with traditional indicators to improve prediction performance. Next, we use the Extreme Gradient Boosting Algorithm (Xgboost), a fast, efficient, and scalable method that has achieved promising results in numerous fields, , to calculate feature importance scores (FISs) and select the most relevant features. Finally, we use support vector regression (SVR), a method that has good generalization and prediction performance for small sample sets, , , to model and effectively improve the coke quality predictions.
After establishing an accurate model, we propose a new algorithm that takes advantage of the problems characteristics. We adopt logistic chaos search in the PSO step, and design a self-adaptive inertia weight adjustment strategy based on sigmoid shrinkage to balance its global and local search abilities. Finally, we obtain the optimal coal blending scheme and cost via modified PSO (MPSO).
The remainder of this paper is organized as follows. Section2 describes how the coal blending and coking process is analyzed. Section3 introduces our new coke quality prediction model, based on Gaussian functions and XgboostCSVR. Section4 proposes our MPSO-based blended coal cost optimization method. Finally, Section5 presents and analyzes our experimental results.
In our coke quality prediction model based on Gaussian functions and Xgboost-CSVR, we regard the average maximum vitrinite reflectance Rmax as a vitrinite reflectance feature, and also extract the peak height (Rh), peak position (Rp), and half -width (Rw), using a feature extraction method based on Gaussian functions. Then, we combine these four vitrinite reflectance features with six traditional indicators to predict the coke quality. We then select the most representative features using
The goal of the coal blending and coking process is to reduce costs under a minimum coke quality constraint. In addition, we need to comply with the steel companys production and procurement plans. Therefore, based on the goal and constraints, we establish a model for optimizing the coal proportions. Since the constraints of the optimization process make it easy to fall into local optima and difficult to obtain an optimal cost and coal blending scheme, we propose to solve the cost optimization
Improving the coke quality and reducing coal blending costs play a vital roles in a many metallurgical industries. However, it is difficult to build models for predicting coke quality due to the multiple feature types involved, their interrelationships, and the nonlinearity of the coal blending and coking process. Meanwhile, the multiple types of constraints make it difficult to obtain optimal coal blending schemes and costs. In this paper, we proposed a model for predicting coke quality based
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
This work aims to improve the synthesis of renewable hydrochar (HC) co-fired with coal to reduce grenhouse gas (GHG) emission. Acetic acid catalyzed hydrothermal carbonization (cHTC) of Chlorella vulgaris microalgae biomass was investigated based on a 331 fractional statistical design of the experiment to examine the effects of hydrothermal reaction temperature (T=180220C), biomass-to-suspension- (BSR=525wt.%), and catalyst-to-suspension (CSR=010 wt.%) ratios on process performance indicators. Analysis of variance was used to assess the experimental data. The results show that the application of homogeneous catalyst improves the fuel ratio and energy recovery efficiency up to 0.38 and 36.3%. Ex-ante cradle-to-gate life cycle assessment was performed to evaluate the impacts of co-firing ratio (CFR) and hydrochar quality on multi-perspective mid-, and endpoint environmental indicators. The highest decarbonization potential (1.54kg CO2,eq kWh1) is achieved using catalytic hydrochar biofuel produced at 195C, 25 wt.% BSR, and 8 wt.% CSR levels. The application of catalytic and autocatalytic hydrochar blends improves the overall environmental impacts and greenhouse gas footprint of solid fuel firing facilitating the transition toward low-carbon emission power generation.
This research presents a new hybridized evolutionary artificial intelligence (AI) model for modeling depth scouring under submerged weir (ds). The proposed model is based on the hybridization of the Extreme Gradient Boosting (XGBoost) model and genetic algorithm (GA) optimizer. The GA is hybridized to solve the hyper-parameter problem of the XGBoost model and to recognize the influential input predictors of ds. The proposed XGBoost-GA model is developed based on the incorporation of fifteen physical parameters of submerged weir. The feasibility of the XGBoost-GA model is validated against several well-established AI models introduced in the literature in addition to a hybrid XGBoost-Grid model. Several statistical performance metrics is computed for the modeling evaluation in parallel with a graphical assessment. Based on the attained prediction results, the proposed model revealed an optimistic and superior predictability performance with a maximum coefficient of determination (R2 = 0.933) and a minimum root mean square error (RMSE = 0.014m). In addition, the XGBoost-GA model demonstrated reliable feature selection for the essential physical parameters. The fifteen parameters are re-scaled to seven parameters based on their essential impacts on the ds determination.
This work proposes a new method to identify operating modes for Continuous Annealing Processes (CAP) based on multiple models consisting of key variables with multi-scale features. The existing operating modes in the CAP are first described in detail, allowing key variables influencing the multiple modes to be selected. The distribution characteristics of each variable are then analyzed to select the corresponding detection methods. Furthermore, the multi-scale features of each variable are then fused to establish multiple models for improving the detectability of the process mode. Finally, a case study based on historical data is performed. The proposed method demonstrated identified different modes in CAP and improved identification performances with fused features.
A novel method for predicting the Gieseler maximum fluidity (Fmax) of a coal+plastic mixture formed from a relative proportion of the plastics present in a multicomponent waste is proposed. A training set of five most-common thermoplastics in household wastes (HDPE, LDPE, PP, PS and PET), binary and ternary plastic mixtures was used to construct multivariable linear regression (MLR) models. Validation was conducted by means of an external set of mixed plastics and real unsorted plastic wastes. The results obtained from the numerical solution of the MLR models were found to be in satisfactory agreement with the experimental data obtained using a Gieseler plastometer. The Fmax values fitted the models with determination coefficients of >0.96 and root mean square errors of prediction of 0.048 and 0.058. All the plastic mixtures tested represented a wide spectrum in concentration of the five polymers contained in municipal plastic wastes and a global plastic addition of 2wt% to the coal was always used. The starting point for this study was to determine the effect of each single plastic on the reduction in fluidity of various coking coals and an industrial coking blend. Afterwards, the exponential functions of Fmax of the blends of coal and binary/ternary plastic mixtures were useful to analyze the changes in Gieseler Fmax with varying proportions of components. Based on the results, the coal responses were statistically treated and MLR models were developed.
The DempsterShafer theory based on multi-SVM to deal with multimodal gesture images for intention understanding is proposed, in which the Sparse Coding (SC) based Speeded-Up Robust Features (SURF) are used for feature extraction of depth and RGB image. Aiming at the problems of the small sample, high dimensionality and feature redundancy for image data, we use the SURF algorithm to extract the features of the original image, and then perform their Sparse Coding, which means that the image is subjected to two-dimensional feature reduction. The dimensionally reduced gesture features are used by the multi-SVM for classification. A fusion framework based on DS evidence theory is constructed to deal with the recognition of depth and RGB image to realize the gesture intention understanding. To verify the effectiveness of the proposal, the experiments on two RGB-D datasets (CGD2011 and CAD-60) are conducted. The results of 10-fold cross validation test show that the recognition rates were higher than those produced by other methods under the condition when each sensor was considered individually. Meanwhile, the preliminary experiments are also carried out in the developing emotional social robot system. The results indicate that the proposal can be applied to humanrobot interaction.
The inferior coking coals as feedstocks to produce char for gasification is an effective way of utilizing the excess coking capacity and the low rank coal. Caking ability of coal is important to the characters and structures of char. For coal blends, it is affected by interactions among coal species. In this study, three different rank coals were selected to explore how the interactions among coals affect the caking ability of blends. The caking ability was evaluated by the caking index (GR.I) and the interactions were analyzed by the comparison between the experimental GR.I and theoretical one. The significantly interactions from greater and less GR.I values of the blends than those of the coals acting independently were observed and they relied on the coal types and ratios in blends. The coal species in blends played the positive effect of the interaction when the experimental GR.I value was greater than 45 of the blends. The increase of fat coal and the decrease of sub-bituminous coal and gas coal strengthened the caking ability and the swelling property of the coal blends. Oxygen content, carbon aromaticity and volatiles of single coal in blends are regarded as the factors to control the interactions of coals and the caking ability of blends.
Operators often make different control decisions for different operating modes to meet the production requirement of the iron ore sintering process. Recognizing the operating modes is important to improve the quality and quantity of the sinter ore. An operating mode recognition method based on the clustering of time series data for the iron ore sintering process is presented in this paper. First, the Spearman rank correlation analysis and the information entropy analysis are combined to select parameters. Next, the operating mode recognition submodel is built by the fuzzy C-Means clustering method based on dynamic time warping distance and the naive Bayesian classifier method. Then, the outputs of the submodels are fused to obtain the final recognized operating mode. Finally, the productivity and combustion efficiency are regarded as the classification criteria, and the raw data collected from an iron and steel plant are used for the experiment. The experimental results show that the proposed method can effectively recognize the operating mode of the sintering process.
This work aimed to evaluate the combined use of charcoal (Ch) and coal tar (Tar) as a way to mitigate the negative effects of using biomass in coal blends for cokemaking. In this way, a greater understanding of the effects of separate addition of charcoal and coal tar on the plasticity of a coking coal (C), as well as the plasticity of the blends with these three materials was achieved. The fluidity of such blends was determined by Giesele plastometry. Thermoplastic tests showed that the addition of charcoal resulted in a depreciation of fluidity, while coal tar assisted in the formation of the liquid phase in the plastic stage, resulting in an increase of fluidity. The results indicate that the combined use of Ch and AL allows for the addition of biomass in cokemaking, without important impacts in the plastic characteristics of the coking coals.
1.Extrusion crushing equipment, such as jaw crusher, rotary crusher, cone crusher, etc. It is suitable for crushing raw materials with a high abrasion index. The content of stone powder in products is low, but generally, the crushed materials have many needle-like particles and poor pumping performance.
2.Impact crushing equipment, such as impact crusher and vertical shaft impact crusher. Its characteristics are large material crushing ratio, simple structure, convenient maintenance of equipment, good product granularity and low loss of material compressive strength.
The common crushers used in sand aggregate production line, their advantages, disadvantages and application scope are as follows:Table of Contents 1. Jaw Crusher2. Gyratory Crusher3. Cone Crusher4. Counter-Impact Crusher5. Hammer Crusher