Computers & Chemical Engineering最新文献_第8页

Point-by-point transfer learning for Bayesian optimization: An accelerated search strategy

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-30 DOI: 10.1016/j.compchemeng.2024.108952

Negareh Mahboubi, Junyao Xie, Biao Huang

引用次数: 0

Improving extrapolation capabilities of a data-driven prediction model for control of an air separation unit

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-30 DOI: 10.1016/j.compchemeng.2024.108953

Valentin Krespach , Nicolas Blum , Martin Pottmann , Sebastian Rehfeldt , Harald Klein

引用次数: 0

Design and analysis of a nuclear and wind-based carbon negative potassium hydroxide water-splitting cycle for hydrogen and ammonia production

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-29 DOI: 10.1016/j.compchemeng.2024.108964

Mert Temiz, Ibrahim Dincer

{"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}

引用次数: 0

ARRTOC: Adversarially Robust Real-Time Optimization and Control 逆向鲁棒实时优化与控制

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-28 DOI: 10.1016/j.compchemeng.2024.108930

Akhil Ahmed, Ehecatl Antonio del Rio-Chanona, Mehmet Mercangöz

{"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}

引用次数: 0

Pressure-swing heterogeneous azeotropic distillation for energy-efficient recovery of ethyl acetate and methanol from wastewater with expanded feed composition range

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-28 DOI: 10.1016/j.compchemeng.2024.108956

Jiaxing Zhu , Ao Yang , Hao Zhang , Weifeng Shen

{"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}

引用次数: 0

Models, modeling and model-based systems in the era of computers, machine learning and AI

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-28 DOI: 10.1016/j.compchemeng.2024.108957

Seyed Soheil Mansouri , Abhishek Sivaram , Christopher J. Savoie , Rafiqul Gani

{"title":"Models, modeling and model-based systems in the era of computers, machine learning and AI","authors":"Seyed Soheil Mansouri , Abhishek Sivaram , Christopher J. Savoie , Rafiqul Gani","doi":"10.1016/j.compchemeng.2024.108957","DOIUrl":"10.1016/j.compchemeng.2024.108957","url":null,"abstract":"<div><div>Models, representing a system under study with respect to problems such as process design, process control, product synthesis and many more, are at the core of most computer-aided solution techniques. The representation of a system through a model is done in different ways, such as, symbols, data, mathematical equations, and/or some combination of these. The workflow or process of creating a proxy mathematical representation (model) of a given target system is referred to as modeling. Model-based software tools incorporate the developed models within the steps of their systematic workflow through simultaneous or decomposed solution strategies related to synthesis, design, analysis, etc., of specific systems. In this perspective paper we highlight the various ways systems can be represented by models, the different ways the required models are developed through modeling techniques, and examples of model-based software tools developed to solve different process and product engineering problems. Two types of systems - process systems and chemical systems, are considered. Important issues and challenges are highlighted and perspectives on how they can be addressed are presented.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108957"},"PeriodicalIF":3.9,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136260","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}

引用次数: 0

A reinforcement learning based Lagrangian relaxation algorithm for multi-energy allocation problem in steel enterprise

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-28 DOI: 10.1016/j.compchemeng.2024.108948

Miao Chang , Shengnan Zhao , Lixin Tang , Jiyin Liu , Yanyan Zhang

{"title":"A reinforcement learning based Lagrangian relaxation algorithm for multi-energy allocation problem in steel enterprise","authors":"Miao Chang , Shengnan Zhao , Lixin Tang , Jiyin Liu , Yanyan Zhang","doi":"10.1016/j.compchemeng.2024.108948","DOIUrl":"10.1016/j.compchemeng.2024.108948","url":null,"abstract":"<div><div>The integrated iron and steel enterprises are typically characterized by the presence of multiple energy media that are highly coupled, frequent start-stop cycles of energy conversion equipment, and fluctuations in energy supply and demand. In this paper, we address the problem of byproduct gas-steam-electricity scheduling in iron and steel enterprises to achieve optimal energy distribution and conversion and reduce the energy cost. This optimization problem for the multi-period full energy chain is formulated as a mathematical programming model that considers equipment start-stop cycles, with the objective of minimizing energy system operating cost. A Lagrangian relaxation framework is employed to decouple the energy management model into several independent single schedules. To further improve the algorithm performance, a novel reinforcement learning-based Lagrangian relaxation algorithm (RL-LR) is proposed, which can dynamically set step size coefficients during the iteration process. Numerical results are presented demonstrating that the RL-LR algorithm can achieve higher optimization efficiency.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108948"},"PeriodicalIF":3.9,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136640","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}

引用次数: 0

Distributionally robust CVaR optimization for refinery integrated production–maintenance scheduling under uncertainty 不确定条件下炼油厂综合生产维护调度的分布鲁棒CVaR优化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-27 DOI: 10.1016/j.compchemeng.2024.108949

Ya Liu , Jiahao Lai , Bo Chen , Kai Wang , Fei Qiao , Hanli Wang

{"title":"Distributionally robust CVaR optimization for refinery integrated production–maintenance scheduling under uncertainty","authors":"Ya Liu , Jiahao Lai , Bo Chen , Kai Wang , Fei Qiao , Hanli Wang","doi":"10.1016/j.compchemeng.2024.108949","DOIUrl":"10.1016/j.compchemeng.2024.108949","url":null,"abstract":"<div><div>In the petroleum refining industry, efficient production planning and maintenance scheduling are crucial for economic performance and operational efficiency. Moreover, the production processes face significant uncertainties stemming from market fluctuations and equipment failures. However, traditional optimization methods often treat production and maintenance independently and neglect the risk management associated with uncertainties in the production process, leading to unreliable plans and suboptimal execution. To address these issues, this paper proposes an innovative data-driven distributionally robust conditional value-at-risk (DRCVaR) method to tackle the integrated production–maintenance optimization problem under crude oil price uncertainty. By constructing confidence sets with <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> norm constraints based on historical data, our approach directly links the model’s conservatism to the amount of available data, effectively managing risk. In addition, we propose robust linear transformation to simplify the min–max nonlinear problem into a conic constraint problem, enhancing solution efficiency and ensuring better operational stability. Refinery case studies demonstrate that the proposed DRCVaR consistently achieves a practical and acceptable solution, significantly outperforming state-of-the-art approaches.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108949"},"PeriodicalIF":3.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747015","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}

引用次数: 0

Machine learning in PEM water electrolysis: A study of hydrogen production and operating parameters PEM水电解中的机器学习：产氢和操作参数的研究

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-27 DOI: 10.1016/j.compchemeng.2024.108954

Ibrahim Shomope , Amani Al-Othman , Muhammad Tawalbeh , Hussam Alshraideh , Fares Almomani

{"title":"Machine learning in PEM water electrolysis: A study of hydrogen production and operating parameters","authors":"Ibrahim Shomope , Amani Al-Othman , Muhammad Tawalbeh , Hussam Alshraideh , Fares Almomani","doi":"10.1016/j.compchemeng.2024.108954","DOIUrl":"10.1016/j.compchemeng.2024.108954","url":null,"abstract":"<div><div>Proton exchange membrane water electrolysis (PEMWE) powered by renewable energy stands out as a promising technology for the sustainable production of high-purity hydrogen. This study employed three machine learning (ML) algorithms, random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGBoost), to predict hydrogen production in PEMWE. Model performance was evaluated using root mean squared error (RMSE), coefficient of determination (<em>R²</em>), and mean absolute error (MAE) metrics. The top-performing models, RF and XGBoost, were further refined through hyperparameter tuning. The final models demonstrated high reliability in predicting hydrogen production rates, with RF consistently outperforming XGBoost. The RF model achieved a predictive accuracy of <em>R²</em> = 0.9898, RMSE = 19.99 mL/min, and MAE = 10.41 mL/min, while the XGBoost model achieved <em>R²</em> = 0.9894, RMSE = 20.43 mL/min, and MAE = 11.50 mL/min. Partial dependency plots (PDPs) emphasized the critical role of optimizing both cell voltage and current to maximize hydrogen production in PEMWE. These insights provide valuable guidance for operational adjustments, ensuring optimal system performance for high efficiency and productivity. The study suggests further research on the impact of parameters like temperature and power density on hydrogen production, incorporating them for better optimization.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108954"},"PeriodicalIF":3.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747016","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}

引用次数: 0

State and parameter estimation in closed-loop dynamic real-time optimization — A comparative study

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2024-11-25 DOI: 10.1016/j.compchemeng.2024.108932

José Matias , Christopher L.E. Swartz

{"title":"State and parameter estimation in closed-loop dynamic real-time optimization — A comparative study","authors":"José Matias , Christopher L.E. Swartz","doi":"10.1016/j.compchemeng.2024.108932","DOIUrl":"10.1016/j.compchemeng.2024.108932","url":null,"abstract":"<div><div>Dynamic real-time optimization (DRTO) schemes have risen in popularity as plant environments have become increasingly dynamic due to globalization and deregulated energy markets. Inclusion of the impact of the plant control system on the predicted response gives rise to closed-loop DRTO (CL-DRTO). To avoid using a potentially inaccurate nominal model in CL-DRTO, this work explores incorporating plant measurements through various model updating strategies: bias update, state estimation, and combined parameter and state estimation, the latter two utilizing moving horizon estimation. The strategies are applied to two case studies, a distillation column and a continuous stirred tank reactor. Our findings suggest that the combined state and parameter estimation approach provides improvement in economic performance and fewer constraint violations when parametric uncertainty affects system dynamics nonlinearly. Conversely, the bias update strategy achieves satisfactory economic performance when the propagation of parameter uncertainty in the dynamic model is linear or mildly nonlinear.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108932"},"PeriodicalIF":3.9,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136261","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}

引用次数: 0