Iman Nowrouzi, Amir H. Mohammadi, Abbas Khaksar Manshad
Foam, as a gas‐in‐liquid colloid, has a higher appearance viscosity than the one of both gas and liquid that form it. Adjusting the mobility ratio of the injected fluid–oil system and increasing gas diffusion in the foam injection process increase oil production. With these properties, foam as an injection fluid in fractured reservoirs has a major effect on oil production from the matrixes and prevents premature production of injection fluid. Surfactants are common foaming agents in injection water. Saponins are known as plant‐derived surfactants for forming stable foam. This feature, along with its cheap price and availability, can make them candidates for enhanced oil recovery (EOR) by the foam injection method. However, the utilization of CO2 as the gaseous phase in foam introduces additional machanisms of CO2 injection to the oil recovery operations. In this assessment, a non‐ionic green surfactant derived from the Anabasis setifera plant was used as a foaming agent, while CO2 served as the gas phase. A series of surface tension tests in CO2 environment were performed to determine the optimal concentration of the surfactant. Foaming tests were performed by a designed foam generator. The produced CO2‐foam was then injected into a fractured carbonate plug with six matrixes (with one horizontal and two vertical fractures). Based on the results, the water–CO2 surface tension was reduced to 20.549 mN/m. The optimum salinity based on the foam stability was 10,000 ppm. The half‐life of the foam was determined to be 40 min. Also, the foam characterization showed that the foamability of the surfactant was favourable for increasing oil production so that by secondary flooding, an oil recovery of more than 66% was achieved from the fractured carbonate plug.
{"title":"A non‐ionic green surfactant extracted from the Anabasis setifera plant for improving bulk properties of CO2‐foam in the process of enhanced oil recovery from carbonate reservoirs","authors":"Iman Nowrouzi, Amir H. Mohammadi, Abbas Khaksar Manshad","doi":"10.1002/cjce.25401","DOIUrl":"https://doi.org/10.1002/cjce.25401","url":null,"abstract":"Foam, as a gas‐in‐liquid colloid, has a higher appearance viscosity than the one of both gas and liquid that form it. Adjusting the mobility ratio of the injected fluid–oil system and increasing gas diffusion in the foam injection process increase oil production. With these properties, foam as an injection fluid in fractured reservoirs has a major effect on oil production from the matrixes and prevents premature production of injection fluid. Surfactants are common foaming agents in injection water. Saponins are known as plant‐derived surfactants for forming stable foam. This feature, along with its cheap price and availability, can make them candidates for enhanced oil recovery (EOR) by the foam injection method. However, the utilization of CO<jats:sub>2</jats:sub> as the gaseous phase in foam introduces additional machanisms of CO<jats:sub>2</jats:sub> injection to the oil recovery operations. In this assessment, a non‐ionic green surfactant derived from the <jats:italic>Anabasis setifera</jats:italic> plant was used as a foaming agent, while CO<jats:sub>2</jats:sub> served as the gas phase. A series of surface tension tests in CO<jats:sub>2</jats:sub> environment were performed to determine the optimal concentration of the surfactant. Foaming tests were performed by a designed foam generator. The produced CO<jats:sub>2</jats:sub>‐foam was then injected into a fractured carbonate plug with six matrixes (with one horizontal and two vertical fractures). Based on the results, the water–CO<jats:sub>2</jats:sub> surface tension was reduced to 20.549 mN/m. The optimum salinity based on the foam stability was 10,000 ppm. The half‐life of the foam was determined to be 40 min. Also, the foam characterization showed that the foamability of the surfactant was favourable for increasing oil production so that by secondary flooding, an oil recovery of more than 66% was achieved from the fractured carbonate plug.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611835","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}
Li Li, Jiayi Wang, Zihe Lian, Hongtao Chen, Jian Li
In an effort to explore the potential energy of biomass and reduce industrial reliance on fossil fuels, this study investigates the liquefaction of poplar wood using supercritical methanol and a CuMgAlOx catalyst. It assesses the composition of liquefied products and performs a comprehensive life‐cycle assessment. Results display that at 360°C, with 1 h of CuMgAlOx, poplar wood's conversion rate reached 98.4%. The proportion of alcoholic compounds in the liquefaction products increased dramatically from 7.99% without a catalyst to 70.81% with it, a rise of 786.23%. Moreover, the process's global warming potential (GWP) intensity is significantly lower at 0.886 gCO2eq/MJ compared to the 93 gCO2eq/MJ from conventional petroleum refining, underscoring its substantial emission reduction potential.
{"title":"The liquefaction characteristics of poplar under CuMgAlOx catalysis in supercritical methanol","authors":"Li Li, Jiayi Wang, Zihe Lian, Hongtao Chen, Jian Li","doi":"10.1002/cjce.25406","DOIUrl":"https://doi.org/10.1002/cjce.25406","url":null,"abstract":"In an effort to explore the potential energy of biomass and reduce industrial reliance on fossil fuels, this study investigates the liquefaction of poplar wood using supercritical methanol and a CuMgAlOx catalyst. It assesses the composition of liquefied products and performs a comprehensive life‐cycle assessment. Results display that at 360°C, with 1 h of CuMgAlOx, poplar wood's conversion rate reached 98.4%. The proportion of alcoholic compounds in the liquefaction products increased dramatically from 7.99% without a catalyst to 70.81% with it, a rise of 786.23%. Moreover, the process's global warming potential (GWP) intensity is significantly lower at 0.886 gCO2eq/MJ compared to the 93 gCO2eq/MJ from conventional petroleum refining, underscoring its substantial emission reduction potential.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611837","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}
Griffin Loebsack, Kang Kang, Ken K.‐C. Yeung, Mattia Bartoli, Franco Berruti, Naomi B. Klinghoffer
Red mud (RM) and non‐woody biomass are both underutilized resources for renewable composite materials, which could be used in environmental decontamination processes. This study aims to investigate the efficacy of co‐pyrolyzing non‐woody biomass with RM to produce a magnetic biochar composite. When pyrolyzed, RM is reduced to magnetic iron while the non‐woody biochar is responsible for the adsorption of organic compounds. Ibuprofen, acetaminophen, methyl orange, and methylene blue were used as test compounds to investigate the overall adsorptive capacity of the composite and to determine the possible adsorption mechanisms of biochar produced from RM pyrolyzed with switch grass, phragmites, rice husk, and miscanthus. The composite produced from a 1 to 1 mixture of RM and miscanthus showed the highest adsorption capacity with 13.8 and 8.34 mg/g of ibuprofen and acetaminophen adsorbed, respectively, which is attributed to its greater ‐interactions as a result of lower surface oxygen sites. Different ratios of RM to biomass were also tested for the production of the miscanthus composite, where it was found that the 1:2 ratio showed the best overall adsorption with 25.9 mg/g removal of acetaminophen, surpassing the miscanthus biochar's at 17.9 mg/g.
{"title":"Magnetic adsorbents from co‐pyrolysis of non‐woody biomass and red mud for water decontamination","authors":"Griffin Loebsack, Kang Kang, Ken K.‐C. Yeung, Mattia Bartoli, Franco Berruti, Naomi B. Klinghoffer","doi":"10.1002/cjce.25407","DOIUrl":"https://doi.org/10.1002/cjce.25407","url":null,"abstract":"Red mud (RM) and non‐woody biomass are both underutilized resources for renewable composite materials, which could be used in environmental decontamination processes. This study aims to investigate the efficacy of co‐pyrolyzing non‐woody biomass with RM to produce a magnetic biochar composite. When pyrolyzed, RM is reduced to magnetic iron while the non‐woody biochar is responsible for the adsorption of organic compounds. Ibuprofen, acetaminophen, methyl orange, and methylene blue were used as test compounds to investigate the overall adsorptive capacity of the composite and to determine the possible adsorption mechanisms of biochar produced from RM pyrolyzed with switch grass, phragmites, rice husk, and miscanthus. The composite produced from a 1 to 1 mixture of RM and miscanthus showed the highest adsorption capacity with 13.8 and 8.34 mg/g of ibuprofen and acetaminophen adsorbed, respectively, which is attributed to its greater ‐interactions as a result of lower surface oxygen sites. Different ratios of RM to biomass were also tested for the production of the miscanthus composite, where it was found that the 1:2 ratio showed the best overall adsorption with 25.9 mg/g removal of acetaminophen, surpassing the miscanthus biochar's at 17.9 mg/g.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611838","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}
Giorgio Rovero, Massimo Curti, Cristina Moliner, Elisabetta Arato
This work summarizes the results of several research projects to demonstrate the possibility of scaling‐up spouted beds by assembling a number of square‐based jet units. Cylindrical vessels with a bottom cone and parallelepiped ones with a frustum base do not differ to a large extent in terms of hydrodynamic features. The existing correlations can be used to predict these square‐based spouted beds. Spouted beds can be very reasonably described as well‐mixed reactors. By assembling several units, it is possible to approach a plug flow state for solids residence time distribution (RTD) in continuous processes. Spouting stability is fully reached when the modules do not interfere each other: this goal is obtained by positioning the downstream units at a certain lower level and having the solids move by overflow; placing internal vertical baffles between contiguous units is the solution. Two hydrodynamical models are proposed to describe single and multiple spouted beds; their predictions help in choosing the best geometrical configuration for the assemblage. A possible application of a multiple spouted bed reactor was envisaged for gasifying pelletized textile residues to generate syngas which can be directly used in situ as a co‐fuel. Two units were built: a single 0.2 m side square‐based spouted bed furnace and the consequent dual stage scale‐up apparatus suitable for an auto thermal process. In the bottom stage, four units work independently to run combustion/gasification of low‐quality solid residues, while the upper stage had a cascade of four modules to run pyrolysis/gasification of selected biomass and obtain valuable secondary products.
{"title":"From circular to square‐based section spouted beds: Scale‐up and design overview of a multistage thermal unit","authors":"Giorgio Rovero, Massimo Curti, Cristina Moliner, Elisabetta Arato","doi":"10.1002/cjce.25381","DOIUrl":"https://doi.org/10.1002/cjce.25381","url":null,"abstract":"This work summarizes the results of several research projects to demonstrate the possibility of scaling‐up spouted beds by assembling a number of square‐based jet units. Cylindrical vessels with a bottom cone and parallelepiped ones with a frustum base do not differ to a large extent in terms of hydrodynamic features. The existing correlations can be used to predict these square‐based spouted beds. Spouted beds can be very reasonably described as well‐mixed reactors. By assembling several units, it is possible to approach a plug flow state for solids residence time distribution (RTD) in continuous processes. Spouting stability is fully reached when the modules do not interfere each other: this goal is obtained by positioning the downstream units at a certain lower level and having the solids move by overflow; placing internal vertical baffles between contiguous units is the solution. Two hydrodynamical models are proposed to describe single and multiple spouted beds; their predictions help in choosing the best geometrical configuration for the assemblage. A possible application of a multiple spouted bed reactor was envisaged for gasifying pelletized textile residues to generate syngas which can be directly used in situ as a co‐fuel. Two units were built: a single 0.2 m side square‐based spouted bed furnace and the consequent dual stage scale‐up apparatus suitable for an auto thermal process. In the bottom stage, four units work independently to run combustion/gasification of low‐quality solid residues, while the upper stage had a cascade of four modules to run pyrolysis/gasification of selected biomass and obtain valuable secondary products.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"366 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570097","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}
This article aims to study the implementation of classical proportional‐integrative (PI) controllers and their coupling with the fuzzy inference systems (FISs) in the act of closed‐circuit grinding (CCG) ball mill system. The system was formed for a multiple‐input multiple‐output (MIMO) system, with two inputs, the feed rate (WF) and speed classifier rotor (VR), and two outputs, a sieve fraction 45 μm (P45) and the amount of material by a weight inside the drum (hold up [HU]). The model was simulated based on experimental processes and control strategies. The fuzzy‐PI controllers were developed on the software, and the data from this process were used to build the database and the necessary knowledge to construct the FIS controllers (with fuzzy rules base 3 × 3 and 5 × 5). Their implementation decreases the error criteria integral of time multiplied by the absolute error (ITAE) and integral of the absolute magnitude of the error (IAE) by 35% and 65%, respectively. Although, applying fuzzy‐PI systems with a smaller rule‐based outcome gives the benefits of implementing the fuzzy logic (FL) but with a smaller oscillatory performance and a minor negative effect on HU control.
{"title":"Development of a MIMO fuzzy inference system—PI controller for a closed‐circuit grinding ball mill circuit","authors":"Bruno Xavier Ferreira, Brunno Ferreira dos Santos","doi":"10.1002/cjce.25390","DOIUrl":"https://doi.org/10.1002/cjce.25390","url":null,"abstract":"This article aims to study the implementation of classical proportional‐integrative (PI) controllers and their coupling with the fuzzy inference systems (FISs) in the act of closed‐circuit grinding (CCG) ball mill system. The system was formed for a multiple‐input multiple‐output (MIMO) system, with two inputs, the feed rate (<jats:italic>W</jats:italic><jats:sub>F</jats:sub>) and speed classifier rotor (<jats:italic>V</jats:italic><jats:sub>R</jats:sub>), and two outputs, a sieve fraction 45 μm (P<jats:sub>45</jats:sub>) and the amount of material by a weight inside the drum (hold up [HU]). The model was simulated based on experimental processes and control strategies. The fuzzy‐PI controllers were developed on the software, and the data from this process were used to build the database and the necessary knowledge to construct the FIS controllers (with fuzzy rules base 3 × 3 and 5 × 5). Their implementation decreases the error criteria integral of time multiplied by the absolute error (ITAE) and integral of the absolute magnitude of the error (IAE) by 35% and 65%, respectively. Although, applying fuzzy‐PI systems with a smaller rule‐based outcome gives the benefits of implementing the fuzzy logic (FL) but with a smaller oscillatory performance and a minor negative effect on HU control.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141569996","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}
With the rapid development of intelligent integration in industrial processes, a challenge emerges. Early failures cannot be detected in a timely manner, potentially leading to significant financial losses. While traditional canonical variate analysis (CVA) methods are effective for dynamic process monitoring, they may lack the flexibility required for early fault detection. To address this challenge, a fault detection method based on canonical variate residual analysis (CVRA) is proposed. CVRA introduces a distinctive residual statistic that preserves critical information about the data. It places heightened focus on the primary components of the data, capturing core features of system changes and enhancing sensitivity to early anomalies. Additionally, by incorporating the geometric properties of the Manhattan distance, it mitigates statistical data errors, thereby improving detection accuracy. Simulation results validate the method's effectiveness in the Tennessee Eastman (TE) process. Furthermore, the successful application of the three‐phase flow facility provides a benchmark for evaluation using real process data.
{"title":"Canonical variate residual analysis for industrial processes fault detection","authors":"Yuting Li, Fei Li, Xiaoqiang Liu","doi":"10.1002/cjce.25399","DOIUrl":"https://doi.org/10.1002/cjce.25399","url":null,"abstract":"With the rapid development of intelligent integration in industrial processes, a challenge emerges. Early failures cannot be detected in a timely manner, potentially leading to significant financial losses. While traditional canonical variate analysis (CVA) methods are effective for dynamic process monitoring, they may lack the flexibility required for early fault detection. To address this challenge, a fault detection method based on canonical variate residual analysis (CVRA) is proposed. CVRA introduces a distinctive residual statistic that preserves critical information about the data. It places heightened focus on the primary components of the data, capturing core features of system changes and enhancing sensitivity to early anomalies. Additionally, by incorporating the geometric properties of the Manhattan distance, it mitigates statistical data errors, thereby improving detection accuracy. Simulation results validate the method's effectiveness in the Tennessee Eastman (TE) process. Furthermore, the successful application of the three‐phase flow facility provides a benchmark for evaluation using real process data.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570094","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}
This work proposes a data‐driven state observation algorithm for nonlinear dynamical systems, when the true state trajectory is not measurable and hence the states information needs to be reconstructed from input and output measurements. Such a reduction is formed by kernel canonical correlation analysis (KCCA), which (i) implicitly maps the available input–output data into a higher‐dimensional feature space, namely the reproducing kernel Hilbert space (RKHS); (ii) finds a projection of the past input–output data and a projection of the future input–output data with maximal correlation; and (iii) identifies the projected inputs and outputs, namely the canonical variates, as the observed states. We adopt a least squares support vector machine (LS‐SVM) formulation for KCCA, which imposes regularization on the vectors that specify the projections and is amenable to convex optimization. We prove theoretically that, based on the statistical consistency of KCCA, the observed states determined by the proposed state observer has a guaranteed correlativity with the actual states (when properly transformed). Furthermore, such observed states, when supplemented with the information of succeeding inputs, can be used to predict the succeeding outputs with guaranteed upper bound on the prediction error. Case studies are performed on two numerical examples and an exothermic continuously stirred tank reactor (CSTR).
{"title":"Data‐driven nonlinear state observation for controlled systems: A kernel method and its analysis","authors":"Moritz Woelk, Wentao Tang","doi":"10.1002/cjce.25403","DOIUrl":"https://doi.org/10.1002/cjce.25403","url":null,"abstract":"This work proposes a data‐driven state observation algorithm for nonlinear dynamical systems, when the true state trajectory is not measurable and hence the states information needs to be reconstructed from input and output measurements. Such a reduction is formed by <jats:italic>kernel canonical correlation analysis</jats:italic> (KCCA), which (i) implicitly maps the available input–output data into a higher‐dimensional feature space, namely the reproducing kernel Hilbert space (RKHS); (ii) finds a projection of the past input–output data and a projection of the future input–output data with maximal correlation; and (iii) identifies the projected inputs and outputs, namely the canonical variates, as the observed states. We adopt a least squares support vector machine (LS‐SVM) formulation for KCCA, which imposes regularization on the vectors that specify the projections and is amenable to convex optimization. We prove theoretically that, based on the statistical consistency of KCCA, the observed states determined by the proposed state observer has a guaranteed <jats:italic>correlativity with the actual states</jats:italic> (when properly transformed). Furthermore, such observed states, when supplemented with the information of succeeding inputs, can be used to predict the succeeding outputs with guaranteed <jats:italic>upper bound on the prediction error</jats:italic>. Case studies are performed on two numerical examples and an exothermic continuously stirred tank reactor (CSTR).","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570096","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}
The use of irregular pore‐scale models to study heavy oil reservoirs with high‐temperature, high‐pressure, and high‐stress characteristics is effective. Previous studies have typically focused on regular models and conventional environmental reservoirs, with limited exploration of irregular models and reservoirs in extreme environments. In investigating the process of water displacing heavy oil within reservoirs under high‐temperature, high‐pressure, and high‐stress conditions at the pore scale, the utilization of the four‐parameter method creates a micro‐scale irregular porous media model. The model systematically considers the variation of physical properties of rocks and heavy oil with temperature. The results indicate that an appropriate increase in water injection rate or a decrease in reservoir contact angle will increase the recovery rate, temperature, and stress of the reservoir. At a displacement time of 0.3 s, with the water injection rate increasing from 0.004 to 0.01 m ∙ s−1, the reservoir's recovery degree experiences an increase of 0.091. Simultaneously, the average temperature and average stress of the reservoir increase by 29.66 K and 1.9464 × 109 N · m−2, respectively. At a displacement time of 0.3 s and with the contact angle decreasing from 2π/3 to π/3, the reservoir's recovery degree increases by 0.44537, and the average temperature and average stress of the reservoir increase by 2.87 K and 1.86 × 108 N · m−2, respectively.
利用不规则孔隙尺度模型研究具有高温、高压和高应力特征的重油储层是有效的。以往的研究通常侧重于规则模型和常规环境油藏，对极端环境下的不规则模型和油藏的探索有限。在研究孔隙尺度高温、高压、高应力条件下油藏内水置换重油的过程时，利用四参数法创建了一个微尺度不规则多孔介质模型。该模型系统地考虑了岩石和重油的物理性质随温度的变化。结果表明，适当提高注水率或减小储层接触角将提高采收率、温度和储层应力。在位移时间为 0.3 s 时，注水速度从 0.004 m ∙ s-1 增加到 0.01 m ∙ s-1，油藏的采收率增加了 0.091。同时，储层的平均温度和平均应力分别增加了 29.66 K 和 1.9464 × 109 N - m-2。当位移时间为 0.3 s，接触角从 2π/3 减小到 π/3 时，储层的恢复度增加了 0.44537，储层的平均温度和平均应力分别增加了 2.87 K 和 1.86 × 108 N - m-2。
{"title":"Simulation of flow and heat transfer in high‐temperature and high‐pressure reservoir based on multi‐physical field coupling model at pore scale","authors":"Hongwei Chen, Zheng Sun, Yang Li, Haoyu Su","doi":"10.1002/cjce.25389","DOIUrl":"https://doi.org/10.1002/cjce.25389","url":null,"abstract":"The use of irregular pore‐scale models to study heavy oil reservoirs with high‐temperature, high‐pressure, and high‐stress characteristics is effective. Previous studies have typically focused on regular models and conventional environmental reservoirs, with limited exploration of irregular models and reservoirs in extreme environments. In investigating the process of water displacing heavy oil within reservoirs under high‐temperature, high‐pressure, and high‐stress conditions at the pore scale, the utilization of the four‐parameter method creates a micro‐scale irregular porous media model. The model systematically considers the variation of physical properties of rocks and heavy oil with temperature. The results indicate that an appropriate increase in water injection rate or a decrease in reservoir contact angle will increase the recovery rate, temperature, and stress of the reservoir. At a displacement time of 0.3 s, with the water injection rate increasing from 0.004 to 0.01 m ∙ s<jats:sup>−1</jats:sup>, the reservoir's recovery degree experiences an increase of 0.091. Simultaneously, the average temperature and average stress of the reservoir increase by 29.66 K and 1.9464 × 10<jats:sup>9</jats:sup> N · m<jats:sup>−2</jats:sup>, respectively. At a displacement time of 0.3 s and with the contact angle decreasing from 2π/3 to π/3, the reservoir's recovery degree increases by 0.44537, and the average temperature and average stress of the reservoir increase by 2.87 K and 1.86 × 10<jats:sup>8</jats:sup> N · m<jats:sup>−2</jats:sup>, respectively.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548583","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}
Chengfei Liu, Chunyu Li, Bo Shu, Hongying Xia, Dafang Liu
In this study, microwave pyrolysis of waste printed circuit boards (WPCBs) was carried out in an inert atmosphere, and the effects of pyrolysis temperature and nitrogen flow rate on the yield and composition of pyrolysis products were investigated. With the increase of pyrolysis temperature, the yield of liquid product increases gradually, and the yield of solid product decreases gradually. At 600°C, the yield of each phase tends to be stable. When the temperature continues to rise, the content of H2 and CO decreases, and the content of C6 ~ C9 in the liquid product decreases. Microwave heating promotes the pyrolysis of brominated epoxy resin, which helps to improve the recovery rate of valuable substances and reduce the environmental impact of waste treatment. This study demonstrates that the microwave pyrolysis of WPCBs in nitrogen atmosphere has great potential in the green recovery process.
本研究在惰性气氛中对废印刷电路板（WPCB）进行了微波热解，考察了热解温度和氮气流速对热解产物产率和组成的影响。随着热解温度的升高，液态产物的产率逐渐增加，固态产物的产率逐渐减少。在 600°C 时，各相的产率趋于稳定。当温度继续升高时，H2 和 CO 的含量减少，液态产物中 C6 ~ C9 的含量也减少。微波加热促进了溴化环氧树脂的热解，有助于提高有价物质的回收率，减少废物处理对环境的影响。该研究表明，氮气环境下微波热解木塑复合板在绿色回收工艺中具有巨大潜力。
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Angan Mukherjee, Samuel Adeyemo, Debangsu Bhattacharyya
In recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.
近几十年来，人们一直在探索利用机器学习（ML）和人工智能（AI）方法进行流程建模应用。然而，不同类型的 ML 模型可能具有截然不同的优缺点，这在为特定应用优化选择特定数据驱动模型以及在模型训练期间估算参数时变得至关重要。本文比较和对比了两种不同类型的数据驱动建模方法，即串联/并联全非线性静态-动态神经网络模型和贝叶斯 ML 方法模型。本研究中考虑的这两类人工智能建模方法在各种性能指标上，特别是在模型稀疏性、预测能力和计算费用方面，都明显优于几种最先进的稳态和动态数据驱动建模技术。针对两个非线性动态化学工程系统，对所提出的模型结构和算法的性能进行了评估，一个是用于丙酮气相裂解生产醋酸酐的塞流反应器，另一个是以单乙醇胺为溶剂进行燃烧后二氧化碳捕集的中试装置。对于二氧化碳捕集试验工厂的验证数据，全非线性静态-动态神经网络的烟气出口温度、流速和二氧化碳浓度的均方根误差（RMSE）分别为 0.05%、1.07% 和 5.0%，而贝叶斯 ML 模型的均方根误差（RMSE）分别为 0.1%、1.75% 和 14.14%。对于塞流反应器数据，当测量数据受到高斯、自相关或交叉相关噪声干扰时，贝叶斯 ML 模型的均方根误差优于全非线性静态-动态神经网络。
{"title":"All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes","authors":"Angan Mukherjee, Samuel Adeyemo, Debangsu Bhattacharyya","doi":"10.1002/cjce.25379","DOIUrl":"https://doi.org/10.1002/cjce.25379","url":null,"abstract":"In recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO<jats:sub>2</jats:sub> capture using monoethanolamine as the solvent. For the validation data from the CO<jats:sub>2</jats:sub> capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO<jats:sub>2</jats:sub> concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.","PeriodicalId":501204,"journal":{"name":"The Canadian Journal of Chemical Engineering","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141514182","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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