Support vector regression modelling and optimization of energy consumption in carbon fiber production line

Golkarnarenji, Gelayol; Naebe, Minoo; Badii, Khashayar; Milani, Abbas S.; Jazar, Reza N.; Khayyam, Hamid

doi:10.1016/j.compchemeng.2017.11.020

Cited by 68 publications

(38 citation statements)

References 43 publications

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“…To estimate the energy consumption of the recycling fan, exhaust fan, drives (Drives 1 and 2) for the second oven, Equation (9) was used [ 17 ].

where P , V , I , and φ are power (W), voltage (415 volts), current (A), and phase difference (120°), respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Badii et al [ 13 ] developed a model to predict density based on PAN precursor and OPF functional groups. Golkarnarenji et al [ 17 ] developed an intelligent predictive model for energy consumption in thermal stabilization process, considering production quality and controlling stochastic defects. That study, however, was carried out for the first zone of stabilization process.…”

Section: Introductionmentioning

confidence: 99%

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Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process

et al. 2018

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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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“…To estimate the energy consumption of the recycling fan, exhaust fan, drives (Drives 1 and 2) for the second oven, Equation (9) was used [ 17 ].

where P , V , I , and φ are power (W), voltage (415 volts), current (A), and phase difference (120°), respectively.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process

et al. 2018

Self Cite

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“…Results showed that SVR had a higher accuracy and less errors than the Back Propagation Neural Network (BPNN) model. In addition, SVR was successfully applied to a toxicity assessment [21], battery life prediction [22,23], chemical production [24,25], financial projection [26,27], and agricultural production [28,29]. Based on excellent regression performance and a wide range of successful application cases of SVR, it was selected to predict the traffic data of the 4G network base stations in public scenes in this study.…”

Section: Introductionmentioning

confidence: 99%

Data Prediction of Mobile Network Traffic in Public Scenes by SOS-vSVR Method

Zheng

Lai

Chen

et al. 2020

Sensors

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Accurate base station traffic data in a public place with large changes in the amount of people could help predict the occurrence of network congestion, which would allow us to effectively allocate network resources. This is of great significance for festival network support, routine maintenance, and resource scheduling. However, there are a few related reports on base station traffic prediction, especially base station traffic prediction in public scenes with fluctuations in people flow. This study proposes a public scene traffic data prediction method, which is based on a v Support Vector Regression (vSVR) algorithm. To achieve optimal prediction of traffic, a symbiotic organisms search (SOS) was adopted to optimize the vSVR parameters. Meanwhile, the optimal input time step was determined through a large number of experiments. Experimental data was obtained at the base station of Huainan Wanda Plaza, in the Anhui province of China, for three months, with the granularity being one hour. To verify the predictive performance of vSVR, the classic regression algorithm extreme learning machine (ELM) and variational Bayesian Linear Regression (vBLR) were used. Their optimal prediction results were compared with vSVR predictions. Experimental results show that the prediction results from SOS-vSVR were the best. Outcomes of this study could provide guidance for preventing network congestion and improving the user experience.

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“…Moreover, pre-treating with chemicals may cause cosmetic damage to filaments and also could lead to fusing of filaments when exposed to temperatures due to the lack of exothermic heat dissipation from fibre surface through filament shaking. In the recent past, studies were conducted to understand the thermal stabilization process using established commercial PAN fibres [20,[28][29][30], however, only few researchers worked on continuous carbon fibre processing where it involves stepwise stabilization close to the industrial manufacturing process [1,31,32]. Nonetheless, up to our knowledge most of the works are confined to thermal stabilization rather than identifying the influence of process variations in this stage on subsequently processed carbon fibres.…”

Section: Introductionmentioning

confidence: 99%

Formation of skin-core in carbon fibre processing: A defect or an effect?

Nunna¹,

Setty²,

Naebe³

2019

Express Polym. Lett.

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The influence of stabilized fibre structure and skin-core formation induced by rapid thermal stabilization of polyacrylonitrile (PAN) on the tensile properties of carbon fibres was investigated. Three sets of samples were prepared by stabilizing PAN fibres under three temperature profiles using a continuous carbon fibre processing line. Initially, the chemical structure and density variations in stabilized fibres were examined with respect to process conditions using Fourier Transform Infrared Spectroscopy (FTIR) and density column methods. Interestingly, while the cyclization and dehydrogenation indices are similar for all the stabilized fibres irrespective of temperature profiles used, the densities of these fibres varied from 1.34 to 1.366 g/cc. Micro-Raman studies showed the existence of structural heterogeneity in the fibres from low temperature (LT) carbonization (I(D)/I(G) ratio of core was ~5.6% higher than the skin) that eventually reduced with high temperature (HT) carbonization because of uniform sp 3 to sp 2 hybridization of carbons. However, modulus mapping revealed heterogeneous storage modulus distribution in the HT carbon fibre cross-section from Trial-2 (storage modulus of core was ~23 GPa less than the skin). Interestingly, this heterogeneity did not show a significant effect on the bulk properties of carbon fibres suggesting skin-core formation is an effect rather than a defect.

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Support vector regression modelling and optimization of energy consumption in carbon fiber production line

Cited by 68 publications

References 43 publications

Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process

Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process

Data Prediction of Mobile Network Traffic in Public Scenes by SOS-vSVR Method

Formation of skin-core in carbon fibre processing: A defect or an effect?

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