Olajide Olukayode Ajala, Joel Olatunbosun Oyelade, E. Oke, O. Oniya, B. K. Adeoye
Abstract Vegetable oils are a crucial source of raw materials for many industries. In order to meet the rising demand for oil on global scale, it has become essential to investigate underutilized plant oilseeds. Hura crepitans seeds are one of the underused plant oilseeds from which oil can be produced via solvent-based extraction. For the purpose of predicting the oil yield from Hura crepitans seeds, the extraction process was modelled using a nonlinear autoregressive exogenous neural network (NARX-NN). The input variables to the model are seed/solvent ratio, extraction temperature and extraction time, while oil yield is the response output variable. NARX-NN model is based on 200 data samples, and model architecture comprises of 3 inputs, 1 hidden layer (with 15 neurons) and 1 output with 2 delay elements. The performance evaluation was carried out to examine the accuracy of the developed model in predicting oil yield from Hura crepitans using different statistical indices. The overall correlation coefficient, R (0.80829), mean square error, MSE (0.0120), root mean square error, RMSE (0.1080) and standard prediction error, SEP (0.1666) show that NARX-NN model can accurately be used for the prediction oil yield from Hura crepitans seeds.
{"title":"A nonlinear autoregressive exogenous neural network (NARX-NN) model for the prediction of solvent-based oil extraction from Hura crepitans seeds","authors":"Olajide Olukayode Ajala, Joel Olatunbosun Oyelade, E. Oke, O. Oniya, B. K. Adeoye","doi":"10.1515/cppm-2022-0032","DOIUrl":"https://doi.org/10.1515/cppm-2022-0032","url":null,"abstract":"Abstract Vegetable oils are a crucial source of raw materials for many industries. In order to meet the rising demand for oil on global scale, it has become essential to investigate underutilized plant oilseeds. Hura crepitans seeds are one of the underused plant oilseeds from which oil can be produced via solvent-based extraction. For the purpose of predicting the oil yield from Hura crepitans seeds, the extraction process was modelled using a nonlinear autoregressive exogenous neural network (NARX-NN). The input variables to the model are seed/solvent ratio, extraction temperature and extraction time, while oil yield is the response output variable. NARX-NN model is based on 200 data samples, and model architecture comprises of 3 inputs, 1 hidden layer (with 15 neurons) and 1 output with 2 delay elements. The performance evaluation was carried out to examine the accuracy of the developed model in predicting oil yield from Hura crepitans using different statistical indices. The overall correlation coefficient, R (0.80829), mean square error, MSE (0.0120), root mean square error, RMSE (0.1080) and standard prediction error, SEP (0.1666) show that NARX-NN model can accurately be used for the prediction oil yield from Hura crepitans seeds.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43229269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract A numerical simulation through computational fluid dynamics is presented on the fluid flow mixing in a flow-focusing microfluidic device with three inlet channels confluence angles of 45, 67.5, and 90°. The effect of various parameters such as aspect ratio (0.5, 1, and 1.5), mixing channel length (1–4 mm), and Reynolds number (1–20) on the mixing efficiency, and the pressure drop are evaluated. The results demonstrate that the increase in mixing efficiency results from an increase in the Reynolds number and aspect ratio for all the angles. In addition, an increase in the pressure drop due to an increase in the Reynolds number and a decrease in the aspect ratio is observed. A longer length of the mixing channel indicates a higher mixing efficiency. The mixing efficiency is more suitable at an angle of 45° among the applied angles in terms of the operational and geometric parameters due to an increase in the contact surface of the flows at the inlet channels junction since the mixing index range is between 0.54 and 1 by varying the mentioned parameters.
{"title":"Numerical simulation of fluid flow mixing in flow-focusing microfluidic devices","authors":"Halimeh Aghaei, A. R. Solaimany Nazar","doi":"10.1515/cppm-2022-0023","DOIUrl":"https://doi.org/10.1515/cppm-2022-0023","url":null,"abstract":"Abstract A numerical simulation through computational fluid dynamics is presented on the fluid flow mixing in a flow-focusing microfluidic device with three inlet channels confluence angles of 45, 67.5, and 90°. The effect of various parameters such as aspect ratio (0.5, 1, and 1.5), mixing channel length (1–4 mm), and Reynolds number (1–20) on the mixing efficiency, and the pressure drop are evaluated. The results demonstrate that the increase in mixing efficiency results from an increase in the Reynolds number and aspect ratio for all the angles. In addition, an increase in the pressure drop due to an increase in the Reynolds number and a decrease in the aspect ratio is observed. A longer length of the mixing channel indicates a higher mixing efficiency. The mixing efficiency is more suitable at an angle of 45° among the applied angles in terms of the operational and geometric parameters due to an increase in the contact surface of the flows at the inlet channels junction since the mixing index range is between 0.54 and 1 by varying the mentioned parameters.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47204993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-01DOI: 10.1515/cppm-2023-frontmatter1
{"title":"Frontmatter","authors":"","doi":"10.1515/cppm-2023-frontmatter1","DOIUrl":"https://doi.org/10.1515/cppm-2023-frontmatter1","url":null,"abstract":"","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135002881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amin Esmaeili, Salar Heydari Shalmani, Azadeh Ebrahimian Pirbazari, Fatemeh Esmaeili Khalil Saraei, Fatemeh-Sadat Tabatabai-Yazdi, Ali Ebrahimian Pirbazari, Ali Derakhshesh
Abstract In this work, we developed a facile method for photocatalytic deposition of cobalt atoms as co-catalyst on TiO2 nanosheets (Co(x)/T) under visible light instead of UV irradiation for the first time. The deposition of cobalt atoms on TNs in the Co(x)/T samples was confirmed by DRS, Raman spectroscopy, photoluminescence, nitrogen physisorption, and TEM analyses. The size of cobalt nanoparticles/cluster dispersed on the TiO2 nanosheets were in the range of 5–20 nm according to TEM results. The PXRD patterns showed that the crystal structure and the anatase phase of TNs were preserved in the Co(x)/T samples after the visible light-assisted deposition process. The Co(x)/T samples showed higher activity compared to pure TiO2 nanosheets for the visible light degradation of tetracycline (TC) as pharmaceutical pollutant due to presence of cobalt co-catalyst. We studied the effect of several parameters on the degradation process and proposed the mechanism of degradation according to quenching experiments results. Due to time-consuming and costly of experimental works, we designed two strong artificial intelligence (AI) models (artificial neural networks (ANN) and neuro-fuzzy inference systems (ANFIS)) to estimate the removal process of TC, and predict the removal percent of TC for new values of inputs before performing experiment. The experimental and computational studies showed that the fabricated photocatalysts are as promising candidates for industrial wastewater treatment to meet environmental regulations and provide a new avenue for practical implications.
{"title":"Pharmaceutical wastewater treatment using TiO2 nanosheets deposited by cobalt co-catalyst as hybrid photocatalysts: combined experimental study and artificial intelligence modeling","authors":"Amin Esmaeili, Salar Heydari Shalmani, Azadeh Ebrahimian Pirbazari, Fatemeh Esmaeili Khalil Saraei, Fatemeh-Sadat Tabatabai-Yazdi, Ali Ebrahimian Pirbazari, Ali Derakhshesh","doi":"10.1515/cppm-2022-0070","DOIUrl":"https://doi.org/10.1515/cppm-2022-0070","url":null,"abstract":"Abstract In this work, we developed a facile method for photocatalytic deposition of cobalt atoms as co-catalyst on TiO2 nanosheets (Co(x)/T) under visible light instead of UV irradiation for the first time. The deposition of cobalt atoms on TNs in the Co(x)/T samples was confirmed by DRS, Raman spectroscopy, photoluminescence, nitrogen physisorption, and TEM analyses. The size of cobalt nanoparticles/cluster dispersed on the TiO2 nanosheets were in the range of 5–20 nm according to TEM results. The PXRD patterns showed that the crystal structure and the anatase phase of TNs were preserved in the Co(x)/T samples after the visible light-assisted deposition process. The Co(x)/T samples showed higher activity compared to pure TiO2 nanosheets for the visible light degradation of tetracycline (TC) as pharmaceutical pollutant due to presence of cobalt co-catalyst. We studied the effect of several parameters on the degradation process and proposed the mechanism of degradation according to quenching experiments results. Due to time-consuming and costly of experimental works, we designed two strong artificial intelligence (AI) models (artificial neural networks (ANN) and neuro-fuzzy inference systems (ANFIS)) to estimate the removal process of TC, and predict the removal percent of TC for new values of inputs before performing experiment. The experimental and computational studies showed that the fabricated photocatalysts are as promising candidates for industrial wastewater treatment to meet environmental regulations and provide a new avenue for practical implications.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49239112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Cream cheese, a popular condiment, is widely used in people’s daily diet and in dessert making. To ensure high-quality cream cheese production, the pH value is generally used as the indicator to determine the end point of cream cheese fermentation. The inoculation time and time-dependent concentrations of biomass, lactose, lactic acid are all crucial for pH prediction. However, the inoculation time could vary for industrial applications with multiple fermenters. Moreover, the inoculation time impact on fermentation has not been investigated. This paper aims to build a cream cheese fermentation model predicting pH. The model includes a semi-batch kinetic model and an artificial neural network (ANN) model. The outcome of the model will help the cream cheese industries understand the inoculation time impact on fermentation time and organise better fermenter scheduling.
{"title":"pH prediction for a semi-batch cream cheese fermentation using a grey-box model","authors":"Shiying Guo, Wei Yu, David I. Wilson, B. Young","doi":"10.1515/cppm-2021-0048","DOIUrl":"https://doi.org/10.1515/cppm-2021-0048","url":null,"abstract":"Abstract Cream cheese, a popular condiment, is widely used in people’s daily diet and in dessert making. To ensure high-quality cream cheese production, the pH value is generally used as the indicator to determine the end point of cream cheese fermentation. The inoculation time and time-dependent concentrations of biomass, lactose, lactic acid are all crucial for pH prediction. However, the inoculation time could vary for industrial applications with multiple fermenters. Moreover, the inoculation time impact on fermentation has not been investigated. This paper aims to build a cream cheese fermentation model predicting pH. The model includes a semi-batch kinetic model and an artificial neural network (ANN) model. The outcome of the model will help the cream cheese industries understand the inoculation time impact on fermentation time and organise better fermenter scheduling.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44675830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this work, the modeling of flow equations and associated transported phenomena in wetted-wire columns (WWC) has been carried out by using the CFD method. The studied processes in this column included the absorption of H2S and CO2 gases from the gas stream by absorbent solution. In this regard, laboratory results were available only for CO2 absorption in a column with a single wire or 109 wires. Moreover, the fact that modeling of a wetted-wire column needs robust hardware. As a result, firstly, the process of CO2 absorption with monoethanolamine (MEA) solution in a column with a wire was modeled by COMSOL Multiphysics version 5.6. Then, the results of various parameters were compared with laboratory results (the error percentage was calculated to be 2.4%). It was observed that by increasing the liquid flow rate, the distance between the beads decreased and beads with larger diameters and higher velocities formed. Meanwhile, for the first time, the temperature profile inside the column was determined along the column, the temperature of the liquid phase increased. The gas stream after a slight increase in temperature, left the column with a temperature close to the incoming liquid. After model validation, other processes were investigated, resulting from changing desired gas for separation or liquid solution. Finally, different absorbents’ abilities were predicted to absorb gaseous pollutants and obtained that in terms of absorption efficiency, second-type alkanolamines perform better than other types in the simultaneous absorption of CO2 and H2S.
{"title":"Modeling of carbon dioxide and hydrogen sulfide pollutants absorption in wetted-wire columns with alkanolamines","authors":"Amin Jasour, R. Alizadeh, Hesam Ahmadian","doi":"10.1515/cppm-2022-0056","DOIUrl":"https://doi.org/10.1515/cppm-2022-0056","url":null,"abstract":"Abstract In this work, the modeling of flow equations and associated transported phenomena in wetted-wire columns (WWC) has been carried out by using the CFD method. The studied processes in this column included the absorption of H2S and CO2 gases from the gas stream by absorbent solution. In this regard, laboratory results were available only for CO2 absorption in a column with a single wire or 109 wires. Moreover, the fact that modeling of a wetted-wire column needs robust hardware. As a result, firstly, the process of CO2 absorption with monoethanolamine (MEA) solution in a column with a wire was modeled by COMSOL Multiphysics version 5.6. Then, the results of various parameters were compared with laboratory results (the error percentage was calculated to be 2.4%). It was observed that by increasing the liquid flow rate, the distance between the beads decreased and beads with larger diameters and higher velocities formed. Meanwhile, for the first time, the temperature profile inside the column was determined along the column, the temperature of the liquid phase increased. The gas stream after a slight increase in temperature, left the column with a temperature close to the incoming liquid. After model validation, other processes were investigated, resulting from changing desired gas for separation or liquid solution. Finally, different absorbents’ abilities were predicted to absorb gaseous pollutants and obtained that in terms of absorption efficiency, second-type alkanolamines perform better than other types in the simultaneous absorption of CO2 and H2S.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42965188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Many methods have been developed for the synthesis of silver nanoparticles (Ag-NPs), yet disadvantages are there to declined their catalytic activity due to the large size with small surface area. Hence, herein, the fungus mediated synthesis of Ag-NPs has been reported. The synthesized Ag-NPs were further characterized by XRD, SEM, EDS, and UV–Vis spectroscopy to study the particle size, surface, crystalline nature, phase formation of Ag-NPs and the produced particles were found to be 41.9 nm. The antibacterial efficiency of synthesized Ag-NPs was examined on various bacteria including Streptococcus pyrogenes, Staphylococcus aureus, Bacillus coagulans, Klebsiella pneumoniae and Corynibacterium glutamicum. The Ag-NPs could be considered as excellent broad-spectrum antibacterial agent. More prominently, the Ag-NPs produced by Aspergillus flavus exhibited potent antibacterial activity against certain pathogens. Bacillus coagulans exhibited maximum zone of inhibition 25.16 ± 0.80 at 80 μg/mL with respective to the standard antibiotic 26.66 ± 1.22 at 30 μg/mL concentration.
{"title":"Green synthesis of silver nanoparticles from Aspergillus flavus and their antibacterial performance","authors":"Dinesh Reddy Gopa, Kalyani Pullapukuri","doi":"10.1515/cppm-2022-0054","DOIUrl":"https://doi.org/10.1515/cppm-2022-0054","url":null,"abstract":"Abstract Many methods have been developed for the synthesis of silver nanoparticles (Ag-NPs), yet disadvantages are there to declined their catalytic activity due to the large size with small surface area. Hence, herein, the fungus mediated synthesis of Ag-NPs has been reported. The synthesized Ag-NPs were further characterized by XRD, SEM, EDS, and UV–Vis spectroscopy to study the particle size, surface, crystalline nature, phase formation of Ag-NPs and the produced particles were found to be 41.9 nm. The antibacterial efficiency of synthesized Ag-NPs was examined on various bacteria including Streptococcus pyrogenes, Staphylococcus aureus, Bacillus coagulans, Klebsiella pneumoniae and Corynibacterium glutamicum. The Ag-NPs could be considered as excellent broad-spectrum antibacterial agent. More prominently, the Ag-NPs produced by Aspergillus flavus exhibited potent antibacterial activity against certain pathogens. Bacillus coagulans exhibited maximum zone of inhibition 25.16 ± 0.80 at 80 μg/mL with respective to the standard antibiotic 26.66 ± 1.22 at 30 μg/mL concentration.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42802571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. López-Pérez, Milagros López-López, C. Núñez-Colín, H. Mukhtar, R. Aguilar-López, V. Peña-Caballero
Abstract This study deals with the problem of estimating the amount of biomass and lactic acid concentration in a lactic acid production process. A continuous stirred tank bioreactor was used for the culture of Lactobacillus helveticus. A nonlinear sliding mode observer is proposed and designed, which gives an estimate of both the biomass and lactic acid concentrations as a function of glucose uptake from the culture medium. Numerical results are given to illustrate the effectiveness of the proposed observer against a standard sliding-mode observer. It was found that the proposed observer worked very well for the benchmark bioreactor model. Also, the numerical results indicated that the proposed estimation methodology was robust to the uncertainties associated with un-modelled dynamics. These new sensing technologies, when coupled to software models, improve performance for smart process control, monitoring, and prediction.
{"title":"A novel nonlinear sliding mode observer to estimate biomass for lactic acid production","authors":"P. López-Pérez, Milagros López-López, C. Núñez-Colín, H. Mukhtar, R. Aguilar-López, V. Peña-Caballero","doi":"10.1515/cppm-2021-0074","DOIUrl":"https://doi.org/10.1515/cppm-2021-0074","url":null,"abstract":"Abstract This study deals with the problem of estimating the amount of biomass and lactic acid concentration in a lactic acid production process. A continuous stirred tank bioreactor was used for the culture of Lactobacillus helveticus. A nonlinear sliding mode observer is proposed and designed, which gives an estimate of both the biomass and lactic acid concentrations as a function of glucose uptake from the culture medium. Numerical results are given to illustrate the effectiveness of the proposed observer against a standard sliding-mode observer. It was found that the proposed observer worked very well for the benchmark bioreactor model. Also, the numerical results indicated that the proposed estimation methodology was robust to the uncertainties associated with un-modelled dynamics. These new sensing technologies, when coupled to software models, improve performance for smart process control, monitoring, and prediction.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46799189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Reactive orange 16 (RO16) is the most widely used azo dye in Textile industry. Complex aromatic structures and resistivity to biological decay caused the dye pollutants incompletely treated by the conventional oxidative methods. The current study presents the electro-Fenton-based advanced oxidation treatment of RO16 dye and the process optimization by Taguchi-based design of experiment (DOE). Using a 500 mL volume lab-scale experimental setup, the process was first studied for the principal operational parameters (initial dye concentration (q); [H2O2]/[Fe+2] (R); current density (ρ); and temperature (T)) effect on decolourization (D R ) and COD removal (C R ). Then, by means of the L16 (44) orthogonal array (OA) formation, standard mean and signal-to-noise (S/N) ratio, the process was optimized for the response variables. The result showed the optimized result at q = 100 mg/L, R = 100, ρ = 8 mA/cm2, and T = 32 °C; with D R and C R as 90.023 and 84.344%, respectively. It was found that the current density affects the process most, followed by [H2O2]/[Fe+2] ratio, initial dye concentration, and temperature i.e., ρ > R > q > T. Also, with the analysis of variance (ANOVA), model equations for D R and C R were developed and its accuracy was verified for experimental results. At optimized conditions, the first order removal rate constants (k a ) were found from batch results. Additionally, the thermodynamic constants (ΔH e , ΔS e , and ΔG b ) were also calculated for the nature of heat-energy involved and temperature effect study on dye degradation. The results showed that the process was thermodynamically feasible, endothermic, and non-spontaneous with a lower energy barrier (E A = 46.7 kJ mol−1).
{"title":"Taguchi L16 (44) orthogonal array-based study and thermodynamics analysis for electro-Fenton process treatment of textile industrial dye","authors":"Imran Ahmad, D. Basu","doi":"10.1515/cppm-2022-0045","DOIUrl":"https://doi.org/10.1515/cppm-2022-0045","url":null,"abstract":"Abstract Reactive orange 16 (RO16) is the most widely used azo dye in Textile industry. Complex aromatic structures and resistivity to biological decay caused the dye pollutants incompletely treated by the conventional oxidative methods. The current study presents the electro-Fenton-based advanced oxidation treatment of RO16 dye and the process optimization by Taguchi-based design of experiment (DOE). Using a 500 mL volume lab-scale experimental setup, the process was first studied for the principal operational parameters (initial dye concentration (q); [H2O2]/[Fe+2] (R); current density (ρ); and temperature (T)) effect on decolourization (D R ) and COD removal (C R ). Then, by means of the L16 (44) orthogonal array (OA) formation, standard mean and signal-to-noise (S/N) ratio, the process was optimized for the response variables. The result showed the optimized result at q = 100 mg/L, R = 100, ρ = 8 mA/cm2, and T = 32 °C; with D R and C R as 90.023 and 84.344%, respectively. It was found that the current density affects the process most, followed by [H2O2]/[Fe+2] ratio, initial dye concentration, and temperature i.e., ρ > R > q > T. Also, with the analysis of variance (ANOVA), model equations for D R and C R were developed and its accuracy was verified for experimental results. At optimized conditions, the first order removal rate constants (k a ) were found from batch results. Additionally, the thermodynamic constants (ΔH e , ΔS e , and ΔG b ) were also calculated for the nature of heat-energy involved and temperature effect study on dye degradation. The results showed that the process was thermodynamically feasible, endothermic, and non-spontaneous with a lower energy barrier (E A = 46.7 kJ mol−1).","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47373040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In oil refineries, fluid catalytic cracking (FCC) is a major unit consisting of several process variables and multiple products. Since FCC units are given prime importance as they are contributing a large share in profits, the optimal operation of FCC is always desirable while considering the changing economic scenarios with respect to the products. However, optimization of FCC is quite challenging due to the complex nature of the process. In this work, using Aspen HYSYS V9® catcracker module, process data of FCC was obtained using central composite design (CCD). Second order regression equations for the selected responses were obtained using Analysis of variance (ANOVA) approach. The interaction effects of feed flow, feed temperature, feed pressure, air blower discharge temperature and catalyst circulation rate on responses (yield of products) were presented. Further, the optimization was performed based on a multi-response optimization technique in the Design expert software and the optimal values of the input variables were obtained for the chosen objectives (representing various operation scenarios). The optimal operation scenarios that were obtained for the objectives were validated successfully. This work highlights the use of statistics based soft computing techniques for the optimization of complex chemical engineering operations such as FCC.
{"title":"Multi-objective optimization of a fluid catalytic cracking unit using response surface methodology","authors":"Anish Thomas, M. P. Pavan Kumar","doi":"10.1515/cppm-2022-0018","DOIUrl":"https://doi.org/10.1515/cppm-2022-0018","url":null,"abstract":"Abstract In oil refineries, fluid catalytic cracking (FCC) is a major unit consisting of several process variables and multiple products. Since FCC units are given prime importance as they are contributing a large share in profits, the optimal operation of FCC is always desirable while considering the changing economic scenarios with respect to the products. However, optimization of FCC is quite challenging due to the complex nature of the process. In this work, using Aspen HYSYS V9® catcracker module, process data of FCC was obtained using central composite design (CCD). Second order regression equations for the selected responses were obtained using Analysis of variance (ANOVA) approach. The interaction effects of feed flow, feed temperature, feed pressure, air blower discharge temperature and catalyst circulation rate on responses (yield of products) were presented. Further, the optimization was performed based on a multi-response optimization technique in the Design expert software and the optimal values of the input variables were obtained for the chosen objectives (representing various operation scenarios). The optimal operation scenarios that were obtained for the objectives were validated successfully. This work highlights the use of statistics based soft computing techniques for the optimization of complex chemical engineering operations such as FCC.","PeriodicalId":9935,"journal":{"name":"Chemical Product and Process Modeling","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48479975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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