Pub Date : 2024-12-04DOI: 10.1016/j.compchemeng.2024.108967
Sulenur Asal , Adem Acir , Ibrahim Dincer
In this present study, a nuclear geothermal hybrid system is designed and integrated three different chloride family hydrogen production cycles: cobalt-chloride, 3-step magnesium-chloride, and 4-step magnesium-chloride cycles. This study aims to investigate the integrated systems with the energy and exergy approaches employing the first and second laws of thermodynamics and compare the hydrogen production potentials of the cycles. The produced heat and electricity by nuclear geothermal hybrid system are employed in the hydrogen production cycles, remained heat and electricity are sent to the community for domestic usage. The nuclear geothermal hybrid system produces 1205.40 MW of heat and 578.56 MW of electricity. The hydrogen production amounts of the Co-Cl, 3- and 4-step Mg-Cl cycles are calculated as 0.27 kg/s, 1.09 kg/s and 0.97 kg/s, respectively. Consequently, the highest overall energy and exergy efficiencies are calculated to be 65.05 % and 88.27 % for the Co-Cl cycle driven by heat only. The integrated nuclear-geothermal hybrid also produces power as a useful output.
{"title":"Comparative assessment of chloride family hydrogen production cycles driven by a nuclear-geothermal energy system","authors":"Sulenur Asal , Adem Acir , Ibrahim Dincer","doi":"10.1016/j.compchemeng.2024.108967","DOIUrl":"10.1016/j.compchemeng.2024.108967","url":null,"abstract":"<div><div>In this present study, a nuclear geothermal hybrid system is designed and integrated three different chloride family hydrogen production cycles: cobalt-chloride, 3-step magnesium-chloride, and 4-step magnesium-chloride cycles. This study aims to investigate the integrated systems with the energy and exergy approaches employing the first and second laws of thermodynamics and compare the hydrogen production potentials of the cycles. The produced heat and electricity by nuclear geothermal hybrid system are employed in the hydrogen production cycles, remained heat and electricity are sent to the community for domestic usage. The nuclear geothermal hybrid system produces 1205.40 MW of heat and 578.56 MW of electricity. The hydrogen production amounts of the Co-Cl, 3- and 4-step Mg-Cl cycles are calculated as 0.27 kg/s, 1.09 kg/s and 0.97 kg/s, respectively. Consequently, the highest overall energy and exergy efficiencies are calculated to be 65.05 % and 88.27 % for the Co-Cl cycle driven by heat only. The integrated nuclear-geothermal hybrid also produces power as a useful output.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108967"},"PeriodicalIF":3.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-03DOI: 10.1016/j.compchemeng.2024.108955
David Krone , Erik Esche , Mirko Skiborowski , Jens-Uwe Repke
An existing approach for optimization-based process synthesis with abstracted phenomena-based building blocks (PBB) is extended by implementing it into a novel MINLP framework with structural screening. Consistency across the multilayer MINLP framework is guaranteed by creating a MathML/XML data model and subsequently exporting the code to the different program parts. The novel framework focuses both on fidelity by implementing thermodynamically sound models and on generality by employing a state-space superstructure that spans a large search space. In order to retain tractability, we insert a structural screening layer which pre-screens based on binary decision variables of the superstructure by graph- and rule-based analyses, penalizing non-physical instances without solution of the underlying MINLP. The MINLP framework is successfully applied on two challenging synthesis tasks to determine the separation of the feed streams of benzene and toluene, as well as of n-pentane, n-hexane, and n-heptane utilizing superstructures with two, respectively four PBB.
{"title":"Optimization-based process synthesis by phenomena-based building blocks and an MINLP framework featuring structural screening","authors":"David Krone , Erik Esche , Mirko Skiborowski , Jens-Uwe Repke","doi":"10.1016/j.compchemeng.2024.108955","DOIUrl":"10.1016/j.compchemeng.2024.108955","url":null,"abstract":"<div><div>An existing approach for optimization-based process synthesis with abstracted phenomena-based building blocks (PBB) is extended by implementing it into a novel MINLP framework with structural screening. Consistency across the multilayer MINLP framework is guaranteed by creating a MathML/XML data model and subsequently exporting the code to the different program parts. The novel framework focuses both on fidelity by implementing thermodynamically sound models and on generality by employing a state-space superstructure that spans a large search space. In order to retain tractability, we insert a structural screening layer which pre-screens based on binary decision variables of the superstructure by graph- and rule-based analyses, penalizing non-physical instances without solution of the underlying MINLP. The MINLP framework is successfully applied on two challenging synthesis tasks to determine the separation of the feed streams of benzene and toluene, as well as of n-pentane, n-hexane, and n-heptane utilizing superstructures with two, respectively four PBB.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108955"},"PeriodicalIF":3.9,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-03DOI: 10.1016/j.compchemeng.2024.108970
Vladimir Mahalec
This paper is an invitation to change the plant modeling paradigm to achieve much easier convergence and ensure model consistency between different incarnations of the plant model, while retaining accuracy on par with the rigorous models. Rigorous physical properties [property/mole] are replaced by [property/mass] approximation at local conditions, making bulk properties much less sensitive to changes in stream composition. This eliminates the need for stream mole fractions and stream compositions can be described by linear component mass flows. Detailed process flow diagram is a common topology for all models. Different incarnations of node models are employed at different levels of abstraction (planning, scheduling, optimization, control, design), ensuring inheritance, consistency and increasing accuracy of solutions as plant model instances move from mass to mass-energy, to mass-energy-and-approximate-stream-properties. After the plant model with approximate properties is solved, they are updated via rigorous thermodynamic methods and the plant model is resolved until convergence.
{"title":"A call for a different plant modelling paradigm and a new generation of software","authors":"Vladimir Mahalec","doi":"10.1016/j.compchemeng.2024.108970","DOIUrl":"10.1016/j.compchemeng.2024.108970","url":null,"abstract":"<div><div>This paper is an invitation to change the plant modeling paradigm to achieve much easier convergence and ensure model consistency between different incarnations of the plant model, while retaining accuracy on par with the rigorous models. Rigorous physical properties [property/mole] are replaced by [property/mass] approximation at local conditions, making bulk properties much less sensitive to changes in stream composition. This eliminates the need for stream mole fractions and stream compositions can be described by linear component mass flows. Detailed process flow diagram is a common topology for all models. Different incarnations of node models are employed at different levels of abstraction (planning, scheduling, optimization, control, design), ensuring inheritance, consistency and increasing accuracy of solutions as plant model instances move from mass to mass-energy, to mass-energy-and-approximate-stream-properties. After the plant model with approximate properties is solved, they are updated via rigorous thermodynamic methods and the plant model is resolved until convergence.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108970"},"PeriodicalIF":3.9,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1016/j.compchemeng.2024.108969
Ronald A. Siegel , Sichen Song , George Lyu
For many drugs exhibiting tolerance, it is not possible to maintain efficacy without intermittently halting administration, enabling the body to restore its sensitivity to the drug. Periodic delivery is thus called for. Here we calculate the optimal periodic delivery protocol to maintain efficacy for the largest fraction of time. Calculations are based on a model assuming that tolerance is due to the buildup of a noncompetitive antagonist. The optimal periodic control starts with a bolus of drug followed by a continuously increasing input rate, with input ultimately halted to allow washout of the antagonist. Variables to be optimized are size of the bolus, duration and “waveform” of the continuous input, and duration of the shutoff phase. This work, which assumes a “hard” threshold for drug efficacy, builds on previous papers in which a “fuzzy” threshold was used for computational convenience. Consequences of varying model parameters are explored.
{"title":"Optimal continuous administration of drugs exhibiting tolerance: A modeling study for linear one compartment pharmacokinetics","authors":"Ronald A. Siegel , Sichen Song , George Lyu","doi":"10.1016/j.compchemeng.2024.108969","DOIUrl":"10.1016/j.compchemeng.2024.108969","url":null,"abstract":"<div><div>For many drugs exhibiting tolerance, it is not possible to maintain efficacy without intermittently halting administration, enabling the body to restore its sensitivity to the drug. Periodic delivery is thus called for. Here we calculate the optimal periodic delivery protocol to maintain efficacy for the largest fraction of time. Calculations are based on a model assuming that tolerance is due to the buildup of a noncompetitive antagonist. The optimal periodic control starts with a bolus of drug followed by a continuously increasing input rate, with input ultimately halted to allow washout of the antagonist. Variables to be optimized are size of the bolus, duration and “waveform” of the continuous input, and duration of the shutoff phase. This work, which assumes a “hard” threshold for drug efficacy, builds on previous papers in which a “fuzzy” threshold was used for computational convenience. Consequences of varying model parameters are explored.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108969"},"PeriodicalIF":3.9,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1016/j.compchemeng.2024.108968
Yujie Hu , Runjie Yao , Lingyu Zhu , Lorenz T. Biegler , Xi Chen
Distillation columns are widely used for separation in industry. To ensure separation stability, it is essential to online monitor the internals performance of a distillation column. The separation efficiency can be evaluated by estimation of Murphree Efficiency of the column. However, as the Murphree Efficiency is affected by both the internals and the tower operating states, it cannot be directly used to represent the internals performance until the influence of state variation influence is excluded. To address this problem, a hybrid method with both the mechanism-based and data-driven models is proposed in this work. Initially, steady-state segment is extracted through a wavelet transform. Then, a mechanism-based model is used to derive the Real-time Murphree Efficiency through parameter estimation and data reconciliation for the extracted steady-state segment. Next, an online and offline two-stage strategy is presented for internals performance detection. In the offline stage, a data-driven Bayesian regression model is developed to correlate the tower states and Murphree Efficiency by assuming stable performance of the internals. While in the online stage, an internal performance index is computed by comparing the Expected Murphree Efficiency, predicted by the Bayesian regression model, and the Real-time Murphree Efficiency developed by the mechanism-based model. Lastly, the proposed method is applied to a phenylenediamine distillation system with three columns, for which, degradation of the packing is effectively monitored.
{"title":"A hybrid method for online monitoring of internals performance in distillation columns","authors":"Yujie Hu , Runjie Yao , Lingyu Zhu , Lorenz T. Biegler , Xi Chen","doi":"10.1016/j.compchemeng.2024.108968","DOIUrl":"10.1016/j.compchemeng.2024.108968","url":null,"abstract":"<div><div>Distillation columns are widely used for separation in industry. To ensure separation stability, it is essential to online monitor the internals performance of a distillation column. The separation efficiency can be evaluated by estimation of Murphree Efficiency of the column. However, as the Murphree Efficiency is affected by both the internals and the tower operating states, it cannot be directly used to represent the internals performance until the influence of state variation influence is excluded. To address this problem, a hybrid method with both the mechanism-based and data-driven models is proposed in this work. Initially, steady-state segment is extracted through a wavelet transform. Then, a mechanism-based model is used to derive the <em>Real-time Murphree Efficiency</em> through parameter estimation and data reconciliation for the extracted steady-state segment. Next, an online and offline two-stage strategy is presented for internals performance detection. In the offline stage, a data-driven Bayesian regression model is developed to correlate the tower states and Murphree Efficiency by assuming stable performance of the internals. While in the online stage, an internal performance index is computed by comparing the <em>Expected Murphree Efficiency,</em> predicted by the Bayesian regression model, and the <em>Real-time Murphree Efficiency</em> developed by the mechanism-based model. Lastly, the proposed method is applied to a phenylenediamine distillation system with three columns, for which, degradation of the packing is effectively monitored.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108968"},"PeriodicalIF":3.9,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-30DOI: 10.1016/j.compchemeng.2024.108952
Negareh Mahboubi, Junyao Xie, Biao Huang
Bayesian optimization (BO) is a prominent “black-box” optimization approach. It makes sequential decisions using a Bayesian model, usually a Gaussian process, to effectively explore the search space of laborious optimization problems. However, BO faces notable challenges, particularly in constructing a reliable model for the optimization task when there are insufficient data available. To address the “cold start” problem and enhance the efficiency of BO, transfer learning appears as a powerful strategy which has gained notable attention recently. This approach aims to expedite the optimization process for a target task by utilizing knowledge accumulated from previous, related source tasks. We provide a novel point-by-point transfer learning with mixture of Gaussians for BO (PPTL-MGBO) technique to improve the speed and efficacy of the optimization process. Through evaluations on both synthetic and real-world datasets, PPTL-MGBO has demonstrated marked advancements in optimizing search efficiency, particularly when dealing with sparse or incomplete target data.
{"title":"Point-by-point transfer learning for Bayesian optimization: An accelerated search strategy","authors":"Negareh Mahboubi, Junyao Xie, Biao Huang","doi":"10.1016/j.compchemeng.2024.108952","DOIUrl":"10.1016/j.compchemeng.2024.108952","url":null,"abstract":"<div><div>Bayesian optimization (BO) is a prominent “black-box” optimization approach. It makes sequential decisions using a Bayesian model, usually a Gaussian process, to effectively explore the search space of laborious optimization problems. However, BO faces notable challenges, particularly in constructing a reliable model for the optimization task when there are insufficient data available. To address the “cold start” problem and enhance the efficiency of BO, transfer learning appears as a powerful strategy which has gained notable attention recently. This approach aims to expedite the optimization process for a target task by utilizing knowledge accumulated from previous, related source tasks. We provide a novel point-by-point transfer learning with mixture of Gaussians for BO (PPTL-MGBO) technique to improve the speed and efficacy of the optimization process. Through evaluations on both synthetic and real-world datasets, PPTL-MGBO has demonstrated marked advancements in optimizing search efficiency, particularly when dealing with sparse or incomplete target data.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108952"},"PeriodicalIF":3.9,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-30DOI: 10.1016/j.compchemeng.2024.108953
Valentin Krespach , Nicolas Blum , Martin Pottmann , Sebastian Rehfeldt , Harald Klein
In model predictive control, fully data-driven prediction models can be used besides common (non-)linear prediction models based on first-principles. Although no process knowledge is required while relying only on sufficient data, they suffer in their extrapolation capability which is shown in the present work for the control of an air separation unit. In order to compensate for the deficits in the extrapolation behavior, a further data source, here a digital twin, is deployed for additional data generation. The plant data set is augmented with the artificially generated data giving rise to a hybrid model in terms of data generation. It is shown that this model can significantly improve the prediction quality in former extrapolation areas of the plant data set. Even conclusions about the uncertainty behavior of the prediction model can be found.
{"title":"Improving extrapolation capabilities of a data-driven prediction model for control of an air separation unit","authors":"Valentin Krespach , Nicolas Blum , Martin Pottmann , Sebastian Rehfeldt , Harald Klein","doi":"10.1016/j.compchemeng.2024.108953","DOIUrl":"10.1016/j.compchemeng.2024.108953","url":null,"abstract":"<div><div>In model predictive control, fully data-driven prediction models can be used besides common (non-)linear prediction models based on first-principles. Although no process knowledge is required while relying only on sufficient data, they suffer in their extrapolation capability which is shown in the present work for the control of an air separation unit. In order to compensate for the deficits in the extrapolation behavior, a further data source, here a digital twin, is deployed for additional data generation. The plant data set is augmented with the artificially generated data giving rise to a hybrid model in terms of data generation. It is shown that this model can significantly improve the prediction quality in former extrapolation areas of the plant data set. Even conclusions about the uncertainty behavior of the prediction model can be found.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108953"},"PeriodicalIF":3.9,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-29DOI: 10.1016/j.compchemeng.2024.108964
Mert Temiz, Ibrahim Dincer
To achieve the net-zero target, clean energy sources, carbon-free fuels, and carbon capture are crucial pieces. The current study develops a new potassium hydroxide-based thermochemical water-splitting cycle and combines it with an ammonia export facility with community and data center. A sodium fast reactor and an offshore wind farm are considered to drive the integrated system to generate hydrogen, ammonia, electricity, heating and cooling. The proposed thermochemical water-splitting cycle uses 591 °C heat with a non-equilibrium reaction to generate hydrogen. The generated hydrogen is further used for ammonia generation via high-pressure ammonia reactor and pressure swing adsorption for nitrogen extraction from air. In the integration, sodium fast reactor provides the required heat to carry out integrated processes, where in high-temperature heat is distributed between the thermochemical cycle and the Rankine cycle, and the recovered heat is utilized in further processes. The proposed system is analyzed from thermodynamic aspects using energy and exergy approaches, supported by a parametric study. In addition, a time-dependent analysis is carried out under varying community and data center loads as well as varying wind speed, for each hour in a typical meteorological year. In the proposed integrated system, 230.4 MW of wind farm, 1 GWth of sodium fast reactor, a thermochemical cycle with 3.6 tonnes/hour hydrogen production and 78 tonnes/hour carbon capture capacities, a two-stage Rankine cycle, ammonia generator, and an absorption refrigeration cycle are considered. The energy and exergy efficiencies of newly developed five-step thermochemical cycle are 45.39 % and 62.78 % when reaction temperatures are considered as 240 °C for hydrogen generation and 591 °C for separation. For the integrated system, the overall energy and exergy efficiencies for the entire year are found as 32.61 % and 28.44 %.
{"title":"Design and analysis of a nuclear and wind-based carbon negative potassium hydroxide water-splitting cycle for hydrogen and ammonia production","authors":"Mert Temiz, Ibrahim Dincer","doi":"10.1016/j.compchemeng.2024.108964","DOIUrl":"10.1016/j.compchemeng.2024.108964","url":null,"abstract":"<div><div>To achieve the net-zero target, clean energy sources, carbon-free fuels, and carbon capture are crucial pieces. The current study develops a new potassium hydroxide-based thermochemical water-splitting cycle and combines it with an ammonia export facility with community and data center. A sodium fast reactor and an offshore wind farm are considered to drive the integrated system to generate hydrogen, ammonia, electricity, heating and cooling. The proposed thermochemical water-splitting cycle uses 591 °C heat with a non-equilibrium reaction to generate hydrogen. The generated hydrogen is further used for ammonia generation via high-pressure ammonia reactor and pressure swing adsorption for nitrogen extraction from air. In the integration, sodium fast reactor provides the required heat to carry out integrated processes, where in high-temperature heat is distributed between the thermochemical cycle and the Rankine cycle, and the recovered heat is utilized in further processes. The proposed system is analyzed from thermodynamic aspects using energy and exergy approaches, supported by a parametric study. In addition, a time-dependent analysis is carried out under varying community and data center loads as well as varying wind speed, for each hour in a typical meteorological year. In the proposed integrated system, 230.4 MW of wind farm, 1 GW<sub>th</sub> of sodium fast reactor, a thermochemical cycle with 3.6 tonnes/hour hydrogen production and 78 tonnes/hour carbon capture capacities, a two-stage Rankine cycle, ammonia generator, and an absorption refrigeration cycle are considered. The energy and exergy efficiencies of newly developed five-step thermochemical cycle are 45.39 % and 62.78 % when reaction temperatures are considered as 240 °C for hydrogen generation and 591 °C for separation. For the integrated system, the overall energy and exergy efficiencies for the entire year are found as 32.61 % and 28.44 %.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108964"},"PeriodicalIF":3.9,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-28DOI: 10.1016/j.compchemeng.2024.108930
Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz
Real-Time Optimization (RTO) plays a crucial role in process operation by determining optimal set-points for lower-level controllers. However, tracking these set-points can be challenging at the control layer due to disturbances, measurement noise, and actuator limitations, leading to a mismatch between expected and achieved RTO benefits. To address this, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm. ARRTOC addresses this issue by finding set-points which are both optimal and inherently robust to implementation errors at the control layers. ARRTOC draws inspiration from adversarial machine learning, offering a novel constrained Adversarially Robust Optimization (ARO) solution applied to the RTO layer. We present several case studies to validate our approach, including a bioreactor, a multi-loop evaporator process, and scenarios involving plant-model mismatch. These studies demonstrate that ARRTOC can improve realized RTO benefits by as much as 50% compared to traditional RTO formulations that do not account for control layer performance.
{"title":"ARRTOC: Adversarially Robust Real-Time Optimization and Control","authors":"Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz","doi":"10.1016/j.compchemeng.2024.108930","DOIUrl":"10.1016/j.compchemeng.2024.108930","url":null,"abstract":"<div><div>Real-Time Optimization (RTO) plays a crucial role in process operation by determining optimal set-points for lower-level controllers. However, tracking these set-points can be challenging at the control layer due to disturbances, measurement noise, and actuator limitations, leading to a mismatch between expected and achieved RTO benefits. To address this, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm. ARRTOC addresses this issue by finding set-points which are both optimal and inherently robust to implementation errors at the control layers. ARRTOC draws inspiration from adversarial machine learning, offering a novel constrained Adversarially Robust Optimization (ARO) solution applied to the RTO layer. We present several case studies to validate our approach, including a bioreactor, a multi-loop evaporator process, and scenarios involving plant-model mismatch. These studies demonstrate that ARRTOC can improve realized RTO benefits by as much as 50% compared to traditional RTO formulations that do not account for control layer performance.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108930"},"PeriodicalIF":3.9,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-28DOI: 10.1016/j.compchemeng.2024.108956
Jiaxing Zhu , Ao Yang , Hao Zhang , Weifeng Shen
This article tends to address the limitations of heterogeneous azeotropic distillation (HAD) for separating Serafimov's class 2.0–2b mixtures, such as ethyl acetate/methanol/water. The feasibility of proposed HAD is constrained by a narrow feed composition range, as thoroughly analyzed through thermodynamic insights in this work. To address these limitations, we propose pressure-swing heterogeneous azeotropic distillation (PSHAD), which allows for a broader application range in feed composition and facilitates heat integration for enhanced economic performance. Thermodynamic insights explore the economic viability and feasibility of PSHAD as feed composition and operating pressure vary. The applicable feed concentration range for PSHAD is determined by liquid-liquid region area and maximum allowable pressure. A parallel genetic algorithm optimizes the processes to minimize total annual cost (TAC). Both PSHAD and the heat-integrated configuration demonstrate superior performance compared to the best process in published literature (i.e., intensified extractive distillation), achieving TAC reductions of 26.46 % and 46.22 %, respectively.
{"title":"Pressure-swing heterogeneous azeotropic distillation for energy-efficient recovery of ethyl acetate and methanol from wastewater with expanded feed composition range","authors":"Jiaxing Zhu , Ao Yang , Hao Zhang , Weifeng Shen","doi":"10.1016/j.compchemeng.2024.108956","DOIUrl":"10.1016/j.compchemeng.2024.108956","url":null,"abstract":"<div><div>This article tends to address the limitations of heterogeneous azeotropic distillation (HAD) for separating Serafimov's class 2.0–2b mixtures, such as ethyl acetate/methanol/water. The feasibility of proposed HAD is constrained by a narrow feed composition range, as thoroughly analyzed through thermodynamic insights in this work. To address these limitations, we propose pressure-swing heterogeneous azeotropic distillation (PSHAD), which allows for a broader application range in feed composition and facilitates heat integration for enhanced economic performance. Thermodynamic insights explore the economic viability and feasibility of PSHAD as feed composition and operating pressure vary. The applicable feed concentration range for PSHAD is determined by liquid-liquid region area and maximum allowable pressure. A parallel genetic algorithm optimizes the processes to minimize total annual cost (TAC). Both PSHAD and the heat-integrated configuration demonstrate superior performance compared to the best process in published literature (i.e., intensified extractive distillation), achieving TAC reductions of 26.46 % and 46.22 %, respectively.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108956"},"PeriodicalIF":3.9,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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