Computers & Chemical Engineering最新文献_第10页

A robust optimization framework for forest biorefineries design considering uncertainties on biomass growth and product selling prices 考虑生物量增长和产品销售价格不确定性的森林生物精炼厂设计稳健优化框架

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-07-01 DOI: 10.1016/j.compchemeng.2023.108256

Bruno Theozzo, Moises Teles dos Santos

引用次数: 0

Physics-based Penalization for Hyperparameter Estimation in Gaussian Process Regression 高斯过程回归中超参数估计的物理惩罚

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-19 DOI: 10.1016/j.compchemeng.2023.108320

Jinhyeun Kim , Christopher Luettgen , Kamran Paynabar , Fani Boukouvala

引用次数: 0

An Efficient Reinforcement Learning Approach to Optimal Control with Application to Biodiesel Production 一种高效的强化学习方法在生物柴油生产中的应用

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.compchemeng.2023.108258

Shiam Kannan , Urmila Diwekar

引用次数: 0

Zone-wise surrogate modelling (ZSM) of univariate systems 单变量系统的区域代理模型(ZSM)

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.compchemeng.2023.108249

Srikar Venkataraman Srinivas, Iftekhar A Karimi

引用次数: 0

Numerical investigation of hydrogen production from low-pressure microwave steam plasma 低压微波蒸汽等离子体制氢的数值研究

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.compchemeng.2023.108230

Oytun Oner, Ibrahim Dincer

{"title":"Numerical investigation of hydrogen production from low-pressure microwave steam plasma","authors":"Oytun Oner, Ibrahim Dincer","doi":"10.1016/j.compchemeng.2023.108230","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2023.108230","url":null,"abstract":"<div><p>Hydrogen is recognized as a suitable energy carrier that can facilitate the storage and transport of renewable energy. In this context, hydrogen production from microwave-excited steam plasma is one of the least researched techniques and lacks numerical study due to the complex plasma kinetics and difficullty of the simulation process. Therefore, in this study, a kinetic model is developed for steam plasma, considering forty-one reactions and fourteen species. Two-dimensional microwave steam plasma is modeled using COMSOL Multiphysics software for the first time. At a microwave power of 800 W, plasma formation and hydrogen production from low pressure (1 Torr) and high temperature (150 °C) steam plasma are numerically studied within the time domain of 10<sup>−10</sup> to 10<sup>−4</sup> s. The presented results demonstrate that the maximum electron density reaches 5 10<sup>17</sup> <em>m</em><sup>−3</sup> at 10<sup>−4</sup>s, and 16.8% of the water molecules dissociate to form various species. The conversion rate of water molecules to hydrogen is calculated as 24%. According to the thermodynamic evaluations, the proposed system's energy and exergy efficiencies are 10.31% and 10.14%, respectively, with a hydrogen production rate of 0.68 μg/s. Furthermore, the effect of the applied microwave power on plasma properties and hydrogen production is parametrically studied. Despite the proportional relationship between the input power and hydrogen production, no correlation is found between microwave power and system efficiency.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"174 ","pages":"Article 108230"},"PeriodicalIF":4.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"1820234","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: 2

A dynamic model of tablet film coating processes for control system design 片剂薄膜涂布过程的动态模型，用于控制系统设计

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.compchemeng.2023.108251

Cecilia Pereira Rodrigues , Carl Duchesne , Éric Poulin , Pierre-Philippe Lapointe-Garant

引用次数: 0

Flexibility analysis using boundary functions for considering dependencies in uncertain parameters 考虑不确定参数依赖性的边界函数柔性分析

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.compchemeng.2023.108231

Christian Langner , Elin Svensson , Stavros Papadokonstantakis , Simon Harvey

{"title":"Flexibility analysis using boundary functions for considering dependencies in uncertain parameters","authors":"Christian Langner , Elin Svensson , Stavros Papadokonstantakis , Simon Harvey","doi":"10.1016/j.compchemeng.2023.108231","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2023.108231","url":null,"abstract":"<div><p>In this work, we present a novel approach for considering dependencies (often called correlations) in the uncertain parameters when performing (deterministic) flexibility analysis. Our proposed approach utilizes (linear) boundary functions to approximate the observed or expected distribution of operating points (i.e. uncertainty space), and can easily be integrated in the flexibility index or flexibility test problem. In contrast to the hyperbox uncertainty sets commonly used in deterministic flexibility analysis, uncertainty sets based on boundary functions allow subsets of the hyperbox which limit the flexibility metric but in which no operation is observed or expected, to be excluded. We derive a generic mixed-integer formulation for the flexibility index based on uncertainty sets defined by boundary functions, and suggest an algorithm to identify boundary functions which approximate the uncertainty set with high accuracy. The approach is tested and compared in several examples including an industrial case study.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"174 ","pages":"Article 108231"},"PeriodicalIF":4.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"1751386","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: 1

A scalable optimization framework for refinery operation and management 一个可扩展的精炼厂运营和管理优化框架

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.compchemeng.2023.108242

Mayank Baranwal , Mayur Selukar , Rushi Lotti , Aditya A. Paranjape , Sushanta Majumder , Jerome Rocher

{"title":"A scalable optimization framework for refinery operation and management","authors":"Mayank Baranwal , Mayur Selukar , Rushi Lotti , Aditya A. Paranjape , Sushanta Majumder , Jerome Rocher","doi":"10.1016/j.compchemeng.2023.108242","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2023.108242","url":null,"abstract":"<div><p>End-to-end refinery management is a complex scheduling problem requiring simultaneous optimization of coupled subprocesses at several stages. In the specific context of this paper, a planner needs to ascertain (i) how best to store incoming crude at a port, (ii) schedule its transfer, after dewatering, to downstream refinery tanks, and (iii) schedule further processing in the crude distillation units (CDUs). The movement and storage of crude is subjected to various physico-chemical and operational constraints. The resulting optimization problem is combinatorial in nature and scales exponentially with the number of tanks, types of crude, and modes of operation. The problem becomes particularly challenging with stochasticity in crude receipt, requiring the planner to modify their decisions in real-time. In this paper, we develop a scalable, hierarchical framework to address the end-to-end refinery management for throughput maximization. The framework relies on an innovative approach to decoupling the decision-making at port and refinery, reducing significantly the complexity of the overall optimization problem. The proposed approach also results in a significant improvement over the schedules generated by an expert human planner for throughput maximization. It takes only a few minutes to execute the entire optimization routine, over a 30 day planning window, on a standard computer, making it possible to use implement our approach in a time-critical, real-time operational setting.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"174 ","pages":"Article 108242"},"PeriodicalIF":4.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"1820126","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 Light Attention-Mixed-Base Deep Learning Architecture toward Process Multivariable Modeling and Knowledge Discovery 面向过程多变量建模和知识发现的轻注意力混合基础深度学习体系结构

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.compchemeng.2023.108259

Yue Li , Lijuan Hu , Ning Li , Weifeng Shen

{"title":"A Light Attention-Mixed-Base Deep Learning Architecture toward Process Multivariable Modeling and Knowledge Discovery","authors":"Yue Li , Lijuan Hu , Ning Li , Weifeng Shen","doi":"10.1016/j.compchemeng.2023.108259","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2023.108259","url":null,"abstract":"<div><p><span><span>A Light Attention-Mixed-Base Deep Learning Architecture (LAMBDA) is developed to simultaneously achieve process knowledge discovery and high-accuracy multivariable modeling. By organizing multiple network bases and a novel light attention mechanism in a special way, the proposed LAMBDA is capable to learn different factors affecting the chemical process outputs, i.e. the basic dynamic characteristics, transient disturbances and other unknown factors. Besides, a development procedure embedding a hyperparameter optimization framework—Optuna is performed to optimize the network architecture. Compared with baselines including </span>FNN<span>, CNN<span>, LSTM and Attention-LSTM, the new architecture displays an outstanding fitting capacity on the discharge </span></span></span>flowrates modeling of an actual deethanization process. The process knowledges extracted from the LAMBDA model parameters are also illustrated, which are valuable in the development of advanced process tasks. The proposed LAMDBA can fit any number of outputs without degrading the knowledge discovery ability, making itself potential in the modeling of complex chemical processes.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"174 ","pages":"Article 108259"},"PeriodicalIF":4.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"2622920","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: 2

Physics Informed Piecewise Linear Neural Networks for Process Optimization 过程优化的物理信息分段线性神经网络

IF 4.3 2区工程技术

Computers & Chemical Engineering Pub Date : 2023-06-01 DOI: 10.1016/j.compchemeng.2023.108244

Ece Serenat Koksal , Erdal Aydin

{"title":"Physics Informed Piecewise Linear Neural Networks for Process Optimization","authors":"Ece Serenat Koksal , Erdal Aydin","doi":"10.1016/j.compchemeng.2023.108244","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2023.108244","url":null,"abstract":"<div><p>Constructing first-principles models is usually a challenging and time-consuming task due to the complexity of real-life processes. On the other hand, data-driven modeling, particularly a neural network model, often suffers from overfitting and lack of useful and high-quality data. At the same time, embedding trained machine learning models directly into the optimization problems has become an effective and state-of-the-art approach for surrogate optimization, whose performance can be improved by physics-informed machine learning. This study proposes using piecewise linear neural network models with physics-informed knowledge for optimization problems with neural network models embedded. In addition to using widely accepted and naturally piecewise linear rectified linear unit (ReLU) activation functions, this study also suggests piecewise linear approximations for the hyperbolic tangent activation function to widen the domain. Optimization of three case studies, a blending process, an industrial distillation column, and a crude oil column are investigated. Physics-informed trained neural network-based optimal results are closer to global optimality for all cases. Finally, associated CPU times for the optimization problems are much shorter than the standard optimization results.</p></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"174 ","pages":"Article 108244"},"PeriodicalIF":4.3,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"1694863","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