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Minoo Naebe - One of the best experts on this subject based on the ideXlab platform.
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pan precursor fabrication applications and thermal stabilization process in Carbon Fiber Production experimental and mathematical modelling
Progress in Materials Science, 2020Co-Authors: Hamid Khayyam, Reza N Jazar, Srinivas Nunna, Gelayol Golkarnarenji, Khashayar Badii, Seyed Mousa Fakhrhoseini, Satish Kumar, Minoo NaebeAbstract:Abstract Polyacrylonitrile (PAN) is a versatile man-made polymer and has been used in a large array of products since its first mass Production in the mid 40s. Among all applications of PAN the widely used application is in manufacture of precursor Fiber for fabrication of Carbon Fibers. The process of PAN-based Carbon Fiber Production comprises Fiber spinning, thermal stabilization and Carbonization stages. Carbon Fiber properties are significantly dependent on the quality of PAN precursor Fiber and in particular the process parameters involved in thermal stabilization. This paper is the first comprehensive review that provides a general understanding of the links between PAN Fiber structure, properties, and its stabilization process along with the use of mathematical modelling as a powerful tool in prediction and optimization of the processes involved. Since the promise of the mathematical modelling is to predict the future behaviour of the system and the value of the variables for the unseen or unmeasured domain of variables; and in the era of industry 4.0 rise, this review will be valuable in further understanding of the intricate processes of Carbon Fiber manufacture and utilising the advanced mathematical modelling using machine learning techniques to predict and optimize a range of critical factors that control the quality of PAN and resultant Carbon Fibers.
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A multi-objective Gaussian process approach for optimization and prediction of Carbonization process in Carbon Fiber Production under uncertainty
Advanced Composites and Hybrid Materials, 2019Co-Authors: Milad Ramezankhani, Hamid Khayyam, Minoo Naebe, Bryn Crawford, Rudolf Seethaler, Abbas S MilaniAbstract:During composite Fiber Production, Carbon Fibers are normally derived from polyacrylonitrile precursor. Carbonization, as a key step of this process, is significantly energy-consuming and costly, owing to its high temperature requirement. A cost-effective approach to optimize energy consumption during the Carbonization is implementing predictive modeling techniques. In this article, a Gaussian process approach has been developed to predict the mechanical properties of Carbon Fibers in the presence of manufacturing uncertainties. The model is also utilized to optimize the Fiber mechanical properties under a minimum energy consumption criterion and a range of process constraints. Finally, as the Young’s modulus and ultimate tensile strength of the Fibers did not show an evident correlation, a multi-objective optimization approach was introduced to acquire the overall optimum condition of the process parameters. To estimate the trade-off between these material properties, the standard as well as an adaptive weighted sum method were applied. Results were summarized as design chart for potential applications by manufacturing process designers. Graphical abstract
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A Pathway to Reduce Energy Consumption in the Thermal Stabilization Process of Carbon Fiber Production
Energies, 2018Co-Authors: Srinivas Nunna, Seyed Mousa Fakhrhoseini, Maxime Maghe, Bhargav Polisetti, Minoo NaebeAbstract:Process parameters, especially in the thermal stabilization of polyacrylonitrile (PAN) Fibers, play a critical role in controlling the cost and properties of the resultant Carbon Fibers. This study aimed to efficiently handle the energy expense areas during Carbon Fiber manufacturing without reducing the quality of Carbon Fibers. We introduced a new parameter (recirculation fan frequency) in the stabilization stage and studied its influence on the evolution of the structure and properties of Fibers. Initially, the progress of the cyclization reaction in the Fiber cross-sections with respect to fan frequencies (35, 45, and 60 Hz) during stabilization was analyzed using the Australian Synchrotron-high resolution infrared imaging technique. A parabolic trend in the evolution of cyclic structures was observed in the Fiber cross-sections during the initial stages of stabilization; however, it was transformed to a uniform trend at the end of stabilization for all fan frequencies. Simultaneously, the microstructure and property variations at each stage of manufacturing were assessed. We identified nominal structural variations with respect to fan frequencies in the intermediate stages of thermal stabilization, which were reduced during the Carbonization process. No statistically significant variations were observed between the tensile properties of Fibers. These observations suggested that, when using a lower fan frequency (35 Hz), it was possible to manufacture Carbon Fibers with a similar performance to those produced using a higher fan frequency (60 Hz). As a result, this study provided an opportunity to reduce the energy consumption during Carbon Fiber manufacturing.
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evaluating the thermal extrusion behavior of a coking coal for direct Carbon Fiber Production
Energy & Fuels, 2018Co-Authors: Rohan Stanger, Minoo Naebe, Quang Anh Tran, Mariah Browne, John Lucas, Murray Height, T F WallAbstract:This study outlines a novel thermal extrusion system and methodologies for evaluating the potential to manufacture Carbon Fiber directly from thermoplastic coals. It is envisioned that the intermediate product will be further refined by spinning down to commercial Fiber sizes and thermal annealing. Commercial melt spinning is used for manufacturing Carbon Fibers from pitch-based feed materials, and a similar approach for plasticized coal is likely to be a lower risk option. However, the critical aspect of using coal for this purpose is its behavior inside a higher pressure extrusion unit and the need to characterize its rheology. This work has evaluated the thermoplastic development needed for extrusion of a single coking coal in terms of the heating rate and residence time and characterized the extruded Fiber product. It was observed that the coal underwent a preliminary softening phase prior to extruding at significant speed. This phase appeared necessary to develop the critical viscosity for extrusion ...
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Production of low cost Carbon Fiber through energy optimization of stabilization process
Materials, 2018Co-Authors: Gelayol Golkarnarenji, Minoo Naebe, Reza N Jazar, Khashayar Badii, Abbas S Milani, Hamid KhayyamAbstract:To produce high quality and low cost Carbon Fiber-based composites, the optimization of the Production process of Carbon Fiber and its properties is one of the main keys. The stabilization process is the most important step in Carbon Fiber Production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN Fiber (OPF) in the second zone of a stabilization oven within a Carbon Fiber Production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving Carbon Fiber manufacturing, for assessing and optimizing different stabilization process conditions at large.
Hamid Khayyam - One of the best experts on this subject based on the ideXlab platform.
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pan precursor fabrication applications and thermal stabilization process in Carbon Fiber Production experimental and mathematical modelling
Progress in Materials Science, 2020Co-Authors: Hamid Khayyam, Reza N Jazar, Srinivas Nunna, Gelayol Golkarnarenji, Khashayar Badii, Seyed Mousa Fakhrhoseini, Satish Kumar, Minoo NaebeAbstract:Abstract Polyacrylonitrile (PAN) is a versatile man-made polymer and has been used in a large array of products since its first mass Production in the mid 40s. Among all applications of PAN the widely used application is in manufacture of precursor Fiber for fabrication of Carbon Fibers. The process of PAN-based Carbon Fiber Production comprises Fiber spinning, thermal stabilization and Carbonization stages. Carbon Fiber properties are significantly dependent on the quality of PAN precursor Fiber and in particular the process parameters involved in thermal stabilization. This paper is the first comprehensive review that provides a general understanding of the links between PAN Fiber structure, properties, and its stabilization process along with the use of mathematical modelling as a powerful tool in prediction and optimization of the processes involved. Since the promise of the mathematical modelling is to predict the future behaviour of the system and the value of the variables for the unseen or unmeasured domain of variables; and in the era of industry 4.0 rise, this review will be valuable in further understanding of the intricate processes of Carbon Fiber manufacture and utilising the advanced mathematical modelling using machine learning techniques to predict and optimize a range of critical factors that control the quality of PAN and resultant Carbon Fibers.
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A multi-objective Gaussian process approach for optimization and prediction of Carbonization process in Carbon Fiber Production under uncertainty
Advanced Composites and Hybrid Materials, 2019Co-Authors: Milad Ramezankhani, Hamid Khayyam, Minoo Naebe, Bryn Crawford, Rudolf Seethaler, Abbas S MilaniAbstract:During composite Fiber Production, Carbon Fibers are normally derived from polyacrylonitrile precursor. Carbonization, as a key step of this process, is significantly energy-consuming and costly, owing to its high temperature requirement. A cost-effective approach to optimize energy consumption during the Carbonization is implementing predictive modeling techniques. In this article, a Gaussian process approach has been developed to predict the mechanical properties of Carbon Fibers in the presence of manufacturing uncertainties. The model is also utilized to optimize the Fiber mechanical properties under a minimum energy consumption criterion and a range of process constraints. Finally, as the Young’s modulus and ultimate tensile strength of the Fibers did not show an evident correlation, a multi-objective optimization approach was introduced to acquire the overall optimum condition of the process parameters. To estimate the trade-off between these material properties, the standard as well as an adaptive weighted sum method were applied. Results were summarized as design chart for potential applications by manufacturing process designers. Graphical abstract
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Production of low cost Carbon Fiber through energy optimization of stabilization process
Materials, 2018Co-Authors: Gelayol Golkarnarenji, Minoo Naebe, Reza N Jazar, Khashayar Badii, Abbas S Milani, Hamid KhayyamAbstract:To produce high quality and low cost Carbon Fiber-based composites, the optimization of the Production process of Carbon Fiber and its properties is one of the main keys. The stabilization process is the most important step in Carbon Fiber Production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN Fiber (OPF) in the second zone of a stabilization oven within a Carbon Fiber Production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving Carbon Fiber manufacturing, for assessing and optimizing different stabilization process conditions at large.
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support vector regression modelling and optimization of energy consumption in Carbon Fiber Production line
Computers & Chemical Engineering, 2018Co-Authors: Gelayol Golkarnarenji, Minoo Naebe, Reza N Jazar, Khashayar Badii, Abbas S Milani, Hamid KhayyamAbstract:Abstract The main chemical industrial efforts are to systematically and continuously explore innovative computing methods of optimizing manufacturing processes to provide better Production quality with lowest cost. Carbon Fiber industry is one of the industries seeks these methods as it provides high Production quality while consuming a lot of energy and being costly. This is due to the fact that the thermal stabilization process consumes a considerable amount of energy. Hence, the aim of this study is to develop an intelligent predictive model for energy consumption in thermal stabilization process, considering Production quality and controlling stochastic defects. The developed and optimized support vector regression (SVR) prediction model combined with genetic algorithm (GA) optimizer yielded a very satisfactory set-up, reducing the energy consumption by up to 43%, under both physical property and skin-core defect constraints. The developed stochastic-SVR-GA approach with limited training data-set offers reduction of energy consumption for similar chemical industries, including Carbon Fiber manufacturing.
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stochastic optimization models for energy management in Carbonization process of Carbon Fiber Production
Applied Energy, 2015Co-Authors: Hamid Khayyam, Minoo Naebe, Alireza Babhadiashar, Farshid Jamshidi, Quanxiang Li, Stephen Atkiss, Derek BuckmasterAbstract:Industrial producers face the task of optimizing Production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial Carbon Fiber Production, the Fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of Fiber are analyzed for their tensile strength and modulus. The energy consumption during Production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov–Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties.
Yongsheng Ding - One of the best experts on this subject based on the ideXlab platform.
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An Immune System-Inspired Reconfigurable Controller
IEEE Transactions on Control Systems Technology, 2016Co-Authors: Yongsheng Ding, Nan Xu, Biao HuangAbstract:Based on the biological immune mechanism, a design approach for the immune reconfigurable controller (IRC) is proposed. Using four units to imitate the immune system's surveillance process, response process, memory mechanism, and self-learning process, respectively, the IRC is capable of actuator fault detection and fault-tolerant control for multi-input multioutput systems. Meanwhile, in order to further improve the control performance, an online optimization process with the multiobjective clonal selection algorithm is designed. To verify its effectiveness, the IRC is applied to the coagulation bath of polyacrylonitrile Carbon Fiber Production line. Comparison experiments with conventional PID and reconfigurable model-based predictive controller control schemes are conducted. The simulation results demonstrate that the IRC can rapidly eliminate the fluctuation due to the actuator fault and guarantees the stability of the coagulation bath. In addition, the IRC has the ability of quick response to the same failure as well as unknown faults.
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the bi directional prediction of Carbon Fiber Production using a combination of improved particle swarm optimization and support vector machine
Materials, 2014Co-Authors: Chuncai Xiao, Yongsheng DingAbstract:This paper creates a bi-directional prediction model to predict the performance of Carbon Fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of Carbon Fiber Production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style Carbon Fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.
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An improved fruit fly optimization algorithm inspired from cell communication mechanism for pre-oxidation process of Carbon Fiber Production
Proceedings of the 33rd Chinese Control Conference, 2014Co-Authors: Chuncai Xiao, Yongsheng DingAbstract:Fruit fly optimization algorithm (FOA) invented recently is a new swarm intelligence method based on fruit fly's foraging behaviors, and has been shown to be competitive with existing evolutionary algorithms, such as particle swarm optimization (PSO). However, there are still some disadvantages in FOA, such as, low convergence precision, easily trapped in a local optimum value at the later evolution stage. Inspired by the cell communication mechanism, we propose an improved FOA (CFOA) by incorporating the information of the global worst, mean and best solution into the search strategy to improve the exploitation. The results from a set of numerical benchmark functions show that CFOA outperforms the FOA in most of the experiments. In other words, the performance of the CFOA has a reasonable performance for the testing benchmark functions. Moreover, we apply the CFOA to optimize the controller for pre-oxidation furnaces in Carbon Fiber Production. Simulation results demonstrate the effectiveness of the CFOA.
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The Optimization of Carbon Fiber Drawing Process Based on Cooperative Immune Clonal Selection Algorithm
Advanced Materials Research, 2013Co-Authors: Jiajia Chen, Yongsheng DingAbstract:Drawing is an important process during Carbon Fiber Production. How to obtain the fittest drawing ratios distribute scheme is a typical multi-objective optimization problem. We propose a novel cooperative immune clonal selection algorithm (CICSA) to obtain the optimal linear density and breaking elongation ratio. The CICSA features in synergetic evolution, clonal operation and mutation operation. Compared with the immune algorithm and the genetic algorithm, it has the best performance in precision and convergence time.
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The Bidirectional Optimization of Carbon Fiber Production by Neural Network with a GA-IPSO Hybrid Algorithm
Mathematical Problems in Engineering, 2013Co-Authors: Jiajia Chen, Yongsheng DingAbstract:A hybrid approach of genetic algorithm (GA) and improved particle swarm optimization (IPSO) is proposed to construct the radial basis function neural network (RNN) for real-time optimizing of the Carbon Fiber manufacture process. For the three-layer RNN, we adopt the nearest neighbor-clustering algorithm to determine the neurons number of the hidden layer. When the appropriate network structure is fixed, we present the GA-IPSO algorithm to tune the parameters of the network, which means the center and the width of the node in the hidden layer and the weight of output layer. We introduce a penalty factor to adjust the velocity and position of the particles to expedite convergence of the PSO. The GA is used to mutate the particles to escape local optimum. Then we employ this network to develop the bidirectional optimization model: in one direction, we take Production parameters as input and properties indices as output; in this case, the model is a Carbon Fiber product performance prediction system; in the other direction, we take properties indices as input and Production parameters as output, and at this situation, the model is a Production scheme design tool for novel style Carbon Fiber. Based on the experimental data, the proposed model is compared to the conventional RBF network and basic PSO method; the research results show its validity and the advantages in dealing with optimization problems.
Ariana Beste - One of the best experts on this subject based on the ideXlab platform.
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ReaxFF Study of the Oxidation of Softwood Lignin in View of Carbon Fiber Production
Energy & Fuels, 2014Co-Authors: Ariana BesteAbstract:We investigate the oxidative, thermal conversion of softwood lignin by performing molecular dynamics simulations based on a reactive force field (ReaxFF). The lignin samples are constructed from coniferyl alcohol units, which are connected through linkages that are randomly selected from a natural distribution of linkages in softwood. The goal of this work is to simulate the oxidative stabilization step during Carbon Fiber Production from lignin precursor. We find that at simulation conditions where stabilization reactions occur, the lignin fragments have already undergone extensive degradation. The 5-5 linkage shows the highest reactivity toward cyclization and dehydrogenation.
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reaxff study of the oxidation of lignin model compounds for the most common linkages in softwood in view of Carbon Fiber Production
Journal of Physical Chemistry A, 2014Co-Authors: Ariana BesteAbstract:Lignin is an underused but major component of biomass. One possible area of utilization is the Production of Carbon Fiber. A necessary processing step is the stabilization of lignin Fiber (typically in an oxygen environment) before high temperature treatment. We investigate oxidative, thermal conversion of lignin using computational methods. Dilignol model compounds for the most common (seven) linkages in softwood are chosen to represent the diverse structure of lignin. We perform molecular dynamics simulation where the potential energy surface is described by a reactive force field (ReaxFF). We calculate overall activation energies for model conversion and reveal initial mechanisms of formaldehyde formation. We record fragmentation patterns and average Carbon oxidation numbers at various temperatures. Most importantly, we identify mechanisms for stabilizing reactions that result in cyclic and rigid connections in softwood lignin Fibers that are necessary for further processing into Carbon Fibers.
Gelayol Golkarnarenji - One of the best experts on this subject based on the ideXlab platform.
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pan precursor fabrication applications and thermal stabilization process in Carbon Fiber Production experimental and mathematical modelling
Progress in Materials Science, 2020Co-Authors: Hamid Khayyam, Reza N Jazar, Srinivas Nunna, Gelayol Golkarnarenji, Khashayar Badii, Seyed Mousa Fakhrhoseini, Satish Kumar, Minoo NaebeAbstract:Abstract Polyacrylonitrile (PAN) is a versatile man-made polymer and has been used in a large array of products since its first mass Production in the mid 40s. Among all applications of PAN the widely used application is in manufacture of precursor Fiber for fabrication of Carbon Fibers. The process of PAN-based Carbon Fiber Production comprises Fiber spinning, thermal stabilization and Carbonization stages. Carbon Fiber properties are significantly dependent on the quality of PAN precursor Fiber and in particular the process parameters involved in thermal stabilization. This paper is the first comprehensive review that provides a general understanding of the links between PAN Fiber structure, properties, and its stabilization process along with the use of mathematical modelling as a powerful tool in prediction and optimization of the processes involved. Since the promise of the mathematical modelling is to predict the future behaviour of the system and the value of the variables for the unseen or unmeasured domain of variables; and in the era of industry 4.0 rise, this review will be valuable in further understanding of the intricate processes of Carbon Fiber manufacture and utilising the advanced mathematical modelling using machine learning techniques to predict and optimize a range of critical factors that control the quality of PAN and resultant Carbon Fibers.
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Production of low cost Carbon Fiber through energy optimization of stabilization process
Materials, 2018Co-Authors: Gelayol Golkarnarenji, Minoo Naebe, Reza N Jazar, Khashayar Badii, Abbas S Milani, Hamid KhayyamAbstract:To produce high quality and low cost Carbon Fiber-based composites, the optimization of the Production process of Carbon Fiber and its properties is one of the main keys. The stabilization process is the most important step in Carbon Fiber Production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN Fiber (OPF) in the second zone of a stabilization oven within a Carbon Fiber Production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving Carbon Fiber manufacturing, for assessing and optimizing different stabilization process conditions at large.
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support vector regression modelling and optimization of energy consumption in Carbon Fiber Production line
Computers & Chemical Engineering, 2018Co-Authors: Gelayol Golkarnarenji, Minoo Naebe, Reza N Jazar, Khashayar Badii, Abbas S Milani, Hamid KhayyamAbstract:Abstract The main chemical industrial efforts are to systematically and continuously explore innovative computing methods of optimizing manufacturing processes to provide better Production quality with lowest cost. Carbon Fiber industry is one of the industries seeks these methods as it provides high Production quality while consuming a lot of energy and being costly. This is due to the fact that the thermal stabilization process consumes a considerable amount of energy. Hence, the aim of this study is to develop an intelligent predictive model for energy consumption in thermal stabilization process, considering Production quality and controlling stochastic defects. The developed and optimized support vector regression (SVR) prediction model combined with genetic algorithm (GA) optimizer yielded a very satisfactory set-up, reducing the energy consumption by up to 43%, under both physical property and skin-core defect constraints. The developed stochastic-SVR-GA approach with limited training data-set offers reduction of energy consumption for similar chemical industries, including Carbon Fiber manufacturing.
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Predictive modelling and multi-objective optimisation in thermal stabilisation process of Carbon fibre
2017Co-Authors: Gelayol GolkarnarenjiAbstract:The aim of this study is to develop a predictive model for energy consumption in stabilization process of Carbon Fiber Production under given range of constrains such as fibre physical and mechanical properties, while controlling the stochastic defects.