Pub Date : 2025-03-01Epub Date: 2024-12-13DOI: 10.1016/j.dche.2024.100203
Guilherme V. Espinosa, Amanda L.T. Brandão
Oxidative dehydrogenation of propane using CO2 (ODPC) is among the most investigated on-purpose processes to meet the increased propylene demand, due to the necessity to reduce CO2 emissions. In this context, the present work simulated an ODPC reactor integrated with chemical looping combustion (CLC) of biogas, which provides the necessary heat, and CO2 capture technology in Aspen Plus. The simulation was evaluated based on economic and sustainability criteria. In addition, a kinetic model was proposed and validated for a sufficient range of operation. It was possible to achieve net present value (NPV) of -14.86 106 US$, over a 15-year operational period, based on current carbon pricing policies. However, the potential profitability of the process was demonstrated by investigating the effects of more favorable carbon credit policies, with an increase from 50 to 120 US$ tCO2eq-1 resulting in a NPV of 164.15 106 US$ and 4 years payback period.
{"title":"Economic and sustainability evaluation of green CO2-assisted propane dehydrogenation design","authors":"Guilherme V. Espinosa, Amanda L.T. Brandão","doi":"10.1016/j.dche.2024.100203","DOIUrl":"10.1016/j.dche.2024.100203","url":null,"abstract":"<div><div>Oxidative dehydrogenation of propane using CO<sub>2</sub> (ODPC) is among the most investigated on-purpose processes to meet the increased propylene demand, due to the necessity to reduce CO<sub>2</sub> emissions. In this context, the present work simulated an ODPC reactor integrated with chemical looping combustion (CLC) of biogas, which provides the necessary heat, and CO<sub>2</sub> capture technology in Aspen Plus. The simulation was evaluated based on economic and sustainability criteria. In addition, a kinetic model was proposed and validated for a sufficient range of operation. It was possible to achieve net present value (NPV) of -14.86 10<sup>6</sup> US$, over a 15-year operational period, based on current carbon pricing policies. However, the potential profitability of the process was demonstrated by investigating the effects of more favorable carbon credit policies, with an increase from 50 to 120 US$ tCO<sub>2</sub>eq<sup>-1</sup> resulting in a NPV of 164.15 10<sup>6</sup> US$ and 4 years payback period.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100203"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-10-09DOI: 10.1016/j.dche.2024.100190
Michael Kreitmeir, Bruno Villela Pedras Lago, Ladislaus Schoenfeld, Sebastian Rehfeldt, Harald Klein
We present a one-dimensional first-principle model for parallel-flow regenerative kilns that accounts for the most important effects. These include the kinetics and thermal effects of the limestone decomposition as well as the heat transfer between the gaseous and solid phases. The model consists of two coupled equation systems for the upper and lower part of the kiln. The results of the model are validated qualitatively and are used to train an artificial neural network that predicts the conversion and the temperature in the crossover channel. The artificial neural network performs very well with values of the root mean squared error that are two to three orders of magnitudes lower than the range covered within the data. Finally, we use a genetic algorithm to optimize the feed mass flows such that the conversion and the fuel efficiency are improved in a Pareto-optimal manner. The results are compared to those of a gradient-based optimization method, which shows the usefulness and validity of the approach with the genetic algorithm.
{"title":"First-principle modeling of parallel-flow regenerative kilns and their optimization with genetic algorithm and gradient-based method","authors":"Michael Kreitmeir, Bruno Villela Pedras Lago, Ladislaus Schoenfeld, Sebastian Rehfeldt, Harald Klein","doi":"10.1016/j.dche.2024.100190","DOIUrl":"10.1016/j.dche.2024.100190","url":null,"abstract":"<div><div>We present a one-dimensional first-principle model for parallel-flow regenerative kilns that accounts for the most important effects. These include the kinetics and thermal effects of the limestone decomposition as well as the heat transfer between the gaseous and solid phases. The model consists of two coupled equation systems for the upper and lower part of the kiln. The results of the model are validated qualitatively and are used to train an artificial neural network that predicts the conversion and the temperature in the crossover channel. The artificial neural network performs very well with values of the root mean squared error that are two to three orders of magnitudes lower than the range covered within the data. Finally, we use a genetic algorithm to optimize the feed mass flows such that the conversion and the fuel efficiency are improved in a Pareto-optimal manner. The results are compared to those of a gradient-based optimization method, which shows the usefulness and validity of the approach with the genetic algorithm.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100190"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142422769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-11-14DOI: 10.1016/j.dche.2024.100197
Lorenz T. Biegler
Recent developments in efficient, large-scale nonlinear optimization strategies have had significants impact on the design and operation of engineering systems with equation-oriented (EO) models. On the other hand, rigorous first-principle procedural (i.e., black-box ’truth’) models may be difficult to incorporate directly within this optimization framework. Instead, black-box models are often substituted by lower fidelity surrogate models that may compromise the optimal solution. To overcome these challenges, Trust Region Filter (TRF) methods have been developed, which combine surrogate models optimization with intermittent sampling of truth models. The TRF approach combines efficient solution strategies with minimal recourse to truth models, and leads to guaranteed convergence to the truth model optimum. This survey paper provides a perspective on the conceptual development and evolution of the TRF method along with a review of applications that demonstrate the effectiveness of the TRF approach. In particular, three cases studies are presented on flowsheet optimization with embedded CFD models for advanced power plants and CO2 capture processes, as well as synthesis of heat exchanger networks with detailed finite-element equipment models.
{"title":"The trust region filter strategy: Survey of a rigorous approach for optimization with surrogate models","authors":"Lorenz T. Biegler","doi":"10.1016/j.dche.2024.100197","DOIUrl":"10.1016/j.dche.2024.100197","url":null,"abstract":"<div><div>Recent developments in efficient, large-scale nonlinear optimization strategies have had significants impact on the design and operation of engineering systems with equation-oriented (EO) models. On the other hand, rigorous first-principle procedural (i.e., black-box ’truth’) models may be difficult to incorporate directly within this optimization framework. Instead, black-box models are often substituted by lower fidelity surrogate models that may compromise the optimal solution. To overcome these challenges, Trust Region Filter (TRF) methods have been developed, which combine surrogate models optimization with intermittent sampling of truth models. The TRF approach combines efficient solution strategies with minimal recourse to truth models, and leads to guaranteed convergence to the truth model optimum. This survey paper provides a perspective on the conceptual development and evolution of the TRF method along with a review of applications that demonstrate the effectiveness of the TRF approach. In particular, three cases studies are presented on flowsheet optimization with embedded CFD models for advanced power plants and CO2 capture processes, as well as synthesis of heat exchanger networks with detailed finite-element equipment models.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100197"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-11-27DOI: 10.1016/j.dche.2024.100199
Oliver J. Fisher , Jhuma Sadhukhan , Thorin Daniel , Jin Xuan
Successfully transitioning to a net-zero and circular carbon economy requires adopting innovative technologies and business models to capture CO2 and convert it into valuable chemicals and materials. Given the high economic costs and limited funding available for this transition, robust economic modelling of potential circular carbon pathways is essential to identify economically viable routes. This study introduces a novel techno-economic analysis (TEA) of producing alcohol ethoxylate (AE7), a valuable surfactant, from industrial flue gas. Traditionally, AE7 is produced by reacting fatty alcohols with ethylene oxide derived from fossil or bio-based sources. This research explores a method using CO2 captured from steel industry flue gas to produce AE7, addressing a notable gap in the literature. It evaluates a thermo-catalytic pathway involving Fischer-Tropsch (FT) synthesis with syngas generated by the reverse-water gas-shift reaction, where CO2 reacts with H2. CO2 conversion rates range around 3% across processing capacities of 25 kt/a, 100 kt/a, and 1000 kt/a. The study finds that the CO2 mass fraction concentration in the process emission is 2.47 × 10–5, compared to 0.13 in the incoming flue gas, highlighting the system's positive environmental impact. A radial basis function neural network was built to forecast the long-term average price of fossil-based and bio-based surfactants to benchmark the results against. Economic analysis reveals that the cost of green hydrogen significantly impacts the minimum selling price (MSP), making cost parity with existing fossil-based surfactants challenging. The lowest MSP of $8.77/kg remains above the long-term forecasted price of $3.75/kg for fossil-based C12–14 AE7. However, Monte Carlo simulations show a 21% probability of achieving a positive net present value (NPV) compared to leading bio-based surfactant alternatives. Sensitivity analyses identify capital costs, the price of low-carbon hydrogen (LCOH), and diesel prices as the most influential factors affecting the MSP. Continued advancements in Fischer-Tropsch catalyst technologies, reductions in green hydrogen costs and growing consumer demand for environmentally friendly products could significantly enhance the economic feasibility of this sustainable approach, paving the way for broader adoption and contributing to a circular carbon economy.
{"title":"Techno-economic analysis and process simulation of alkoxylated surfactant production in a circular carbon economy framework","authors":"Oliver J. Fisher , Jhuma Sadhukhan , Thorin Daniel , Jin Xuan","doi":"10.1016/j.dche.2024.100199","DOIUrl":"10.1016/j.dche.2024.100199","url":null,"abstract":"<div><div>Successfully transitioning to a net-zero and circular carbon economy requires adopting innovative technologies and business models to capture CO<sub>2</sub> and convert it into valuable chemicals and materials. Given the high economic costs and limited funding available for this transition, robust economic modelling of potential circular carbon pathways is essential to identify economically viable routes. This study introduces a novel techno-economic analysis (TEA) of producing alcohol ethoxylate (AE7), a valuable surfactant, from industrial flue gas. Traditionally, AE7 is produced by reacting fatty alcohols with ethylene oxide derived from fossil or bio-based sources. This research explores a method using CO<sub>2</sub> captured from steel industry flue gas to produce AE7, addressing a notable gap in the literature. It evaluates a thermo-catalytic pathway involving Fischer-Tropsch (FT) synthesis with syngas generated by the reverse-water gas-shift reaction, where CO<sub>2</sub> reacts with H<sub>2</sub>. CO<sub>2</sub> conversion rates range around 3% across processing capacities of 25 kt/a, 100 kt/a, and 1000 kt/a. The study finds that the CO<sub>2</sub> mass fraction concentration in the process emission is 2.47 × 10<sup>–5</sup>, compared to 0.13 in the incoming flue gas, highlighting the system's positive environmental impact. A radial basis function neural network was built to forecast the long-term average price of fossil-based and bio-based surfactants to benchmark the results against. Economic analysis reveals that the cost of green hydrogen significantly impacts the minimum selling price (MSP), making cost parity with existing fossil-based surfactants challenging. The lowest MSP of $8.77/kg remains above the long-term forecasted price of $3.75/kg for fossil-based C<sub>12–14</sub> AE7. However, Monte Carlo simulations show a 21% probability of achieving a positive net present value (NPV) compared to leading bio-based surfactant alternatives. Sensitivity analyses identify capital costs, the price of low-carbon hydrogen (LCOH), and diesel prices as the most influential factors affecting the MSP. Continued advancements in Fischer-Tropsch catalyst technologies, reductions in green hydrogen costs and growing consumer demand for environmentally friendly products could significantly enhance the economic feasibility of this sustainable approach, paving the way for broader adoption and contributing to a circular carbon economy.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100199"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-09-16DOI: 10.1016/j.dche.2024.100186
Tejennour Tizaoui , Ruomu Tan
The field of process automation possesses a substantial corpus of textual documentation that can be leveraged with Large Language Models (LLMs) and Natural Language Understanding (NLU) systems. Recent advancements in diverse LLMs, available in open source, present an opportunity to utilize them effectively in this area. However, LLMs are pre-trained on general textual data and lack knowledge in more specialized and niche areas such as process automation. Furthermore, the lack of datasets specifically tailored to process automation makes it difficult to assess the effectiveness of LLMs in this domain accurately. This paper aims to lay the foundation for creating a multitask benchmark for evaluating and adapting LLMs in process automation. In the paper, we introduce a novel workflow for semi-automated data generation, specifically tailored to creating extractive Question Answering (QA) datasets. The proposed methodology in this paper involves extracting passages from academic papers focusing on process automation, generating corresponding questions, and subsequently annotating and evaluating the dataset. The dataset initially created also undergoes data augmentation and is evaluated using metrics for semantic similarity. This study then benchmarked six LLMs on the newly created extractive QA dataset for process automation.
{"title":"Towards a benchmark dataset for large language models in the context of process automation","authors":"Tejennour Tizaoui , Ruomu Tan","doi":"10.1016/j.dche.2024.100186","DOIUrl":"10.1016/j.dche.2024.100186","url":null,"abstract":"<div><div>The field of process automation possesses a substantial corpus of textual documentation that can be leveraged with Large Language Models (LLMs) and Natural Language Understanding (NLU) systems. Recent advancements in diverse LLMs, available in open source, present an opportunity to utilize them effectively in this area. However, LLMs are pre-trained on general textual data and lack knowledge in more specialized and niche areas such as process automation. Furthermore, the lack of datasets specifically tailored to process automation makes it difficult to assess the effectiveness of LLMs in this domain accurately. This paper aims to lay the foundation for creating a multitask benchmark for evaluating and adapting LLMs in process automation. In the paper, we introduce a novel workflow for semi-automated data generation, specifically tailored to creating extractive Question Answering (QA) datasets. The proposed methodology in this paper involves extracting passages from academic papers focusing on process automation, generating corresponding questions, and subsequently annotating and evaluating the dataset. The dataset initially created also undergoes data augmentation and is evaluated using metrics for semantic similarity. This study then benchmarked six LLMs on the newly created extractive QA dataset for process automation.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100186"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000486/pdfft?md5=41f0a659b6aed87235c44fe3a8cc7489&pid=1-s2.0-S2772508124000486-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-10-12DOI: 10.1016/j.dche.2024.100194
He Wen , Faisal Khan
The conflicts stemming from discrepancies between human and artificial intelligence (AI) in observation, interpretation, and action have gained attention. Recent publications highlight the seriousness of the concern stemming from conflict and models to identify and assess the conflict risk. No work has been reported on systematically studying how to resolve human and artificial intelligence conflicts. This paper presents a novel approach to model the resolution strategies of human-AI conflicts. This approach reinterprets the conventional human conflict resolution mechanisms within AI. The study proposes a unique mathematical model to quantify conflict risks and delineate effective resolution strategies to minimize conflict risk. The proposed approach and mode are applied to control a two-phase separator system, a major component of a processing facility. The proposed approach promotes the development of robust AI systems with enhanced real-time responses to human inputs. It provides a platform to foster human-AI collaborative engagement and a mechanism of intelligence augmentation.
{"title":"A risk-based model for human-artificial intelligence conflict resolution in process systems","authors":"He Wen , Faisal Khan","doi":"10.1016/j.dche.2024.100194","DOIUrl":"10.1016/j.dche.2024.100194","url":null,"abstract":"<div><div>The conflicts stemming from discrepancies between human and artificial intelligence (AI) in observation, interpretation, and action have gained attention. Recent publications highlight the seriousness of the concern stemming from conflict and models to identify and assess the conflict risk. No work has been reported on systematically studying how to resolve human and artificial intelligence conflicts. This paper presents a novel approach to model the resolution strategies of human-AI conflicts. This approach reinterprets the conventional human conflict resolution mechanisms within AI. The study proposes a unique mathematical model to quantify conflict risks and delineate effective resolution strategies to minimize conflict risk. The proposed approach and mode are applied to control a two-phase separator system, a major component of a processing facility. The proposed approach promotes the development of robust AI systems with enhanced real-time responses to human inputs. It provides a platform to foster human-AI collaborative engagement and a mechanism of intelligence augmentation.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100194"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-10-24DOI: 10.1016/j.dche.2024.100196
AmirMohammad Ebrahimi, Davood B. Pourkargar
This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.
{"title":"Multi-agent distributed control of integrated process networks using an adaptive community detection approach","authors":"AmirMohammad Ebrahimi, Davood B. Pourkargar","doi":"10.1016/j.dche.2024.100196","DOIUrl":"10.1016/j.dche.2024.100196","url":null,"abstract":"<div><div>This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100196"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-09-07DOI: 10.1016/j.dche.2024.100181
Fakhrony Sholahudin Rohman , Sharifah Rafidah Wan Alwi , Dinie Muhammad , Ashraf Azmi , Zainuddin Abd Manan , Jeng Shiun Lim , Hong An Er , Siti Nor Azreen Ahmad Termizi
Optimization on an industrial scale is a complex task that involves fine-tuning the performance of large-scale systems and applications to make them more efficient and effective. This process can be challenging due to the increasing volume of work, growing system complexity, and the need to maintain optimal performance. Due to the significant power required for compression and the high costs of reactant materials, optimizing low-density polyethylene (LDPE) production to provide maximum productivity with a reduction of energy cost is required. However, it is not a simple process because the optimization problem of the LDPE tubular reactor consists of conflicting objective functions. Multi-objective neural network algorithm (MONNA) is a metaheuristic optimization method that provides a versatile and robust approach for solving complex, contradictory targets and diverse optimization problems that do not rely on specific mathematical properties of the problem. It is inspired by the structure and information-processing capabilities of biological neural networks. MONNA iteratively proposes solutions, evaluates its performance, and adjusts its approach based on feedback, which avoids complex mathematical formulations. In this work, we implement Multi-objective optimization neural network algorithm (MONNA) in LDPE tubular reactor for maximising productivity, conversion and minimising energy costs with three scenario of problem optimization, i.e. maximising productivity and reducing energy cost for the first problem (P1); increasing conversion and reducing energy costs for the second problem (P2); and increasing productivity and reducing by-products for the third problem (P3). The results show that the highest productivity, highest conversion, and lowest energy are 545.1 mil. RM/year, 0.314, and 0.672 mil. RM/year. The extreme points in the Pareto Front (PF) for various bi-objective situations provide practitioners with helpful information for selecting the best trade-off for the operational strategy. According to their preferences, decision-makers can use the resulting Pareto to decide on the most acceptable alternative. The decision variable plots show that both initiators in the reacting zone highly affected the optimal solution with the opposite action.
{"title":"Application of multi-objective neural network algorithm in industrial polymerization reactors for reducing energy cost and maximising productivity","authors":"Fakhrony Sholahudin Rohman , Sharifah Rafidah Wan Alwi , Dinie Muhammad , Ashraf Azmi , Zainuddin Abd Manan , Jeng Shiun Lim , Hong An Er , Siti Nor Azreen Ahmad Termizi","doi":"10.1016/j.dche.2024.100181","DOIUrl":"10.1016/j.dche.2024.100181","url":null,"abstract":"<div><p>Optimization on an industrial scale is a complex task that involves fine-tuning the performance of large-scale systems and applications to make them more efficient and effective. This process can be challenging due to the increasing volume of work, growing system complexity, and the need to maintain optimal performance. Due to the significant power required for compression and the high costs of reactant materials, optimizing low-density polyethylene (LDPE) production to provide maximum productivity with a reduction of energy cost is required. However, it is not a simple process because the optimization problem of the LDPE tubular reactor consists of conflicting objective functions. Multi-objective neural network algorithm (MONNA) is a metaheuristic optimization method that provides a versatile and robust approach for solving complex, contradictory targets and diverse optimization problems that do not rely on specific mathematical properties of the problem. It is inspired by the structure and information-processing capabilities of biological neural networks. MONNA iteratively proposes solutions, evaluates its performance, and adjusts its approach based on feedback, which avoids complex mathematical formulations. In this work, we implement Multi-objective optimization neural network algorithm (MONNA) in LDPE tubular reactor for maximising productivity, conversion and minimising energy costs with three scenario of problem optimization, i.e. maximising productivity and reducing energy cost for the first problem (P1); increasing conversion and reducing energy costs for the second problem (P2); and increasing productivity and reducing by-products for the third problem (P3). The results show that the highest productivity, highest conversion, and lowest energy are 545.1 mil. RM/year, 0.314, and 0.672 mil. RM/year. The extreme points in the Pareto Front (PF) for various bi-objective situations provide practitioners with helpful information for selecting the best trade-off for the operational strategy. According to their preferences, decision-makers can use the resulting Pareto to decide on the most acceptable alternative. The decision variable plots show that both initiators in the reacting zone highly affected the optimal solution with the opposite action.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100181"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000437/pdfft?md5=27aff72e7416d069827b52afc2ad31fb&pid=1-s2.0-S2772508124000437-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-10-03DOI: 10.1016/j.dche.2024.100189
Veit Schagon, Rohit Murali, Ruosi Zhang, Melis Duyar, Michael Short
Microbreweries have greater production costs per litre of beer compared to large breweries, as well as higher carbon footprints. Due to the range of different retrofit technologies available and the different capacities and configurations of microbreweries, it is not always clear what retrofits will improve operations. Therefore, this work proposes a novel mixed-integer nonlinear programming decision-making tool to be used by any microbrewery, that determines the technoeconomic feasibility and sizing of energy efficiency-improving retrofits, including solar and wind power, battery storage, anaerobic digestion, boiler type selection, heat integration by heat storage, and carbon capture via dual-function materials. The model was demonstrated on a real UK microbrewery case study. The model gave an optimal configuration of a 10 m3 anaerobic digester, 30 solar panels outputting 380 W each, an 800 W wind turbine and a 2.3 m3 heat storage tank, reducing annual operating costs by 62.9 % and carbon dioxide emissions by 77.1 % with a payback period of 8 years. The tool is designed to be flexible for use by any microbrewery in any location with any brewing recipe and allow the owner(s) to develop more profitable and sustainable microbreweries.
Tweetable abstract
Microbreweries can implement mathematically optimised renewable energy, heat integration and anaerobic digestion to reduce operating costs by 62.9 % and carbon emissions by 77.1 %.
{"title":"An MINLP-based decision-making tool to help microbreweries improve energy efficiency and reduce carbon footprint through retrofits","authors":"Veit Schagon, Rohit Murali, Ruosi Zhang, Melis Duyar, Michael Short","doi":"10.1016/j.dche.2024.100189","DOIUrl":"10.1016/j.dche.2024.100189","url":null,"abstract":"<div><div>Microbreweries have greater production costs per litre of beer compared to large breweries, as well as higher carbon footprints. Due to the range of different retrofit technologies available and the different capacities and configurations of microbreweries, it is not always clear what retrofits will improve operations. Therefore, this work proposes a novel mixed-integer nonlinear programming decision-making tool to be used by any microbrewery, that determines the technoeconomic feasibility and sizing of energy efficiency-improving retrofits, including solar and wind power, battery storage, anaerobic digestion, boiler type selection, heat integration by heat storage, and carbon capture via dual-function materials. The model was demonstrated on a real UK microbrewery case study. The model gave an optimal configuration of a 10 m<sup>3</sup> anaerobic digester, 30 solar panels outputting 380 W each, an 800 W wind turbine and a 2.3 m<sup>3</sup> heat storage tank, reducing annual operating costs by 62.9 % and carbon dioxide emissions by 77.1 % with a payback period of 8 years. The tool is designed to be flexible for use by any microbrewery in any location with any brewing recipe and allow the owner(s) to develop more profitable and sustainable microbreweries.</div><div>Tweetable abstract</div><div>Microbreweries can implement mathematically optimised renewable energy, heat integration and anaerobic digestion to reduce operating costs by 62.9 % and carbon emissions by 77.1 %.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100189"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-10-22DOI: 10.1016/j.dche.2024.100195
Feiyang Ou , Henrik Wang , Chao Zhang , Matthew Tom , Sthitie Bom , James F. Davis , Panagiotis D. Christofides
<div><div>Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial setti
{"title":"Industrial data-driven machine learning soft sensing for optimal operation of etching tools","authors":"Feiyang Ou , Henrik Wang , Chao Zhang , Matthew Tom , Sthitie Bom , James F. Davis , Panagiotis D. Christofides","doi":"10.1016/j.dche.2024.100195","DOIUrl":"10.1016/j.dche.2024.100195","url":null,"abstract":"<div><div>Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial setti","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100195"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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