Digital Chemical Engineering最新文献

{"title":"Robust simulation and technical evaluation of large-scale gas oil hydrocracking process via extended water-energy-product (E-WEP) analysis","authors":"Sofía García-Maza,&nbsp;Ángel Darío González-Delgado","doi":"10.1016/j.dche.2024.100193","DOIUrl":"10.1016/j.dche.2024.100193","url":null,"abstract":"<div><div>Currently, the implementation of techniques to improve the quality of refining products such as hydrocracking of gas oil requires a rigorous analysis of the operating conditions of the system, mainly because at the plant operation level it is difficult to make relevant modifications in the processes without considering the possible economic, environmental, and social impacts that may be generated. For this reason, the need has arisen to use specialized computational tools that allow predicting the behavior of various processes to optimize their stages. This work presents the modeling, simulation, and extended Water-Energy-Product (E-WEP) technical evaluation of the gas oil hydrocracking process on an industrial scale considering the general conditions of the system and the extended development of the material and energy balance, using the Aspen HYSYS® simulator. The results showed that for a load capacity of 487,545 lb/h of gas oil with 145,708 lb/h of hydrogen a Production Yield of 95.77 % was obtained. Finally, 12 technical indicators related to raw materials, products, water, and energy were calculated, where the efficiency of these parameters was determined, reaching the maximum efficiency in the Total Cost of Energy (TCE) indicator with a value of 98.96 %, and the minimum in Wastewater Production Ratio (WPR) with a value of 22.39 %, the latter shows that the process supports mass integration of water effluents.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100193"},"PeriodicalIF":3.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531727","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}

{"title":"A risk-based model for human-artificial intelligence conflict resolution in process systems","authors":"He Wen ,&nbsp;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":"2024-10-12","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}

{"title":"First-principle modeling of parallel-flow regenerative kilns and their optimization with genetic algorithm and gradient-based method","authors":"Michael Kreitmeir,&nbsp;Bruno Villela Pedras Lago,&nbsp;Ladislaus Schoenfeld,&nbsp;Sebastian Rehfeldt,&nbsp;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":"2024-10-09","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}

{"title":"Dynamic feed scheduling for optimised anaerobic digestion: An optimisation approach for better decision-making to enhance revenue and environmental benefits","authors":"Meshkat Dolat ,&nbsp;Rohit Murali ,&nbsp;Mohammadamin Zarei ,&nbsp;Ruosi Zhang ,&nbsp;Tararag Pincam ,&nbsp;Yong-Qiang Liu ,&nbsp;Jhuma Sadhukhan ,&nbsp;Angela Bywater ,&nbsp;Michael Short","doi":"10.1016/j.dche.2024.100191","DOIUrl":"10.1016/j.dche.2024.100191","url":null,"abstract":"<div><div>Anaerobic digestion (AD) offers a sustainable solution for clean energy production, with the potential for significant revenue enhancement through enhanced decision-making. However, the complexity and limited flexibility of AD systems pose challenges in developing reliable optimisation methods. Changing feeding strategies provides opportunities for efficient feedstock utilisation and optimal gas production, especially in volatile gas markets.</div><div>To provide better decision-making tools in AD for energy production, we propose an integrated site model for the dynamic behaviour of the AD process in a biomethane-to-grid system and optimise production based on predicted gas prices. The model includes methods for optimal feed co-digestion strategies and integrates these results into a scheduling model to identify the optimal feedstock acquisition, feeding pattern, and potential gas storage operation considering feedstock availability, properties, sustainability, and fluctuating gas demand under different pricing variations.</div><div>The methodology was tested on a 150 tonnes per day farm-scale AD plant in the UK, processing energy crops and manure considering both environmental (global warming potential) and economic objectives. The results showed strong adaptability of the proposed feeding schedule to the general trend of gas prices over time. To address the challenge of immediate price peaks, typically unattainable due to the system's sluggish behaviour and high retention times, the impacts of on-site storage were explored, leading to annual revenue increases ranging from 2 % to 7.4 %, depending on the pricing scheme, which translates to a significant boost in terms of revenue.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100191"},"PeriodicalIF":3.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441016","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}

{"title":"An MINLP-based decision-making tool to help microbreweries improve energy efficiency and reduce carbon footprint through retrofits","authors":"Veit Schagon,&nbsp;Rohit Murali,&nbsp;Ruosi Zhang,&nbsp;Melis Duyar,&nbsp;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 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.</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":"2024-10-03","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}

{"title":"A hybrid BOA-SVR approach for predicting aerobic organic and nitrogen removal in a gas-liquid-solid circulating fluidized bed bioreactor","authors":"Shaikh Abdur Razzak ,&nbsp;Nahid Sultana ,&nbsp;S.M. Zakir Hossain ,&nbsp;Muhammad Muhitur Rahman ,&nbsp;Yue Yuan ,&nbsp;Mohammad Mozahar Hossain ,&nbsp;Jesse Zhu","doi":"10.1016/j.dche.2024.100188","DOIUrl":"10.1016/j.dche.2024.100188","url":null,"abstract":"<div><div>This study introduces the hybrid of the Bayesian optimization algorithm and support vector regression (BOA-SVR) models to predict the removal of aerobic organic (total chemical oxygen demand, COD) and nitrogen compounds such as total Kjeldahl Nitrogen (TKN), ammonium nitrogen (NH₄-N), and nitrate nitrogen (NO₃-N) from municipal wastewater in a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. GLSCFB bioreactors treat wastewater by removing nutrients biologically. The downer of a GLSCFB bioreactor provided experimental data on TKN, NH₄-N, NO₃-N, and TCOD removal. The hybrid optimal intelligence algorithm (BOA-SVR) has improved model accuracy across multiple domains by combining BOA and SVR. The coefficient of determination (R²), residual, mean absolute error (MAE), root mean square error (RMSE), and fractional bias (FB) were used to analyze BOA-SVR model performance. The models match experimental data from four operational stages well, with R² or adj R² values above 0.99 for all responses. The model's accuracy was confirmed by relative deviations and residual plots showing dispersion around the zero-reference line. The BOA-SVR model consistently predicted dependent variables with low RMSE and MAE values (≤ 2.24 and 2.21, respectively) and near-zero FB. Computing efficiency was shown by optimizing TCOD, TKN, NH4-N, and NO3-N models in 70.61, 72.89, 74.37, and 54.07 s. A rigorous test on unseen data with different noise levels confirmed the model's stability. Furthermore, BOA-SVR performs better than traditional multiple linear regression (MLR). Overall, the BOA-SVR model predicts biological nutrient removal in municipal wastewater utilizing a GLSCFB bioreactor quickly, correctly, and efficiently, reducing experimental stress and resource use.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100188"},"PeriodicalIF":3.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326665","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}

{"title":"Towards a benchmark dataset for large language models in the context of process automation","authors":"Tejennour Tizaoui ,&nbsp;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":"2024-09-16","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}

{"title":"Batch distillation performance improvement through vessel holdup redistribution—Insights from two case studies","authors":"Surendra Beniwal,&nbsp;Sujit S. Jogwar","doi":"10.1016/j.dche.2024.100187","DOIUrl":"10.1016/j.dche.2024.100187","url":null,"abstract":"<div><div>Middle vessel batch distillation (MVBD) is an energy-efficient configuration for separation of a ternary mixture. This paper focuses on improving the performance of this configuration through dynamic optimization of vessel holdup. Initially, a performance measure accounting for separation and energy efficiency is defined to characterize an operational policy. Subsequently, this measure is maximized by dynamically redistributing holdup in the three (top, middle and bottom) vessels. With the help of two case studies, the impact of various policy decisions and market conditions (such as initial feed distribution and relative cost of products and energy) on the optimal operating policy is analyzed. Specifically, the improvement obtained via holdup redistribution is explained with the help of fundamental concepts of distillation. Lastly, the performance of the proposed approach is compared with some of the existing methods and validated through rigorous simulations.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100187"},"PeriodicalIF":3.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000498/pdfft?md5=6501e564d8b86aff39c1f1d2619b5f04&pid=1-s2.0-S2772508124000498-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311196","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}

{"title":"Computational fluid dynamics (CFD)- deep neural network (DNN) model to predict hydrodynamic parameters in rectangular and cylindrical bubble columns","authors":"Vishal Dhakane,&nbsp;Praneet Mishra,&nbsp;Ashutosh Yadav","doi":"10.1016/j.dche.2024.100185","DOIUrl":"10.1016/j.dche.2024.100185","url":null,"abstract":"<div><div>Bubble columns are omnipresent in the chemical, bio-chemical, petrochemicals, petroleum industries, but their design and scale-up is complex owing to its complex hydrodynamics. Liquid velocity and gas holdup is one of the critical hydrodynamic parameters which effects the mixing, heat and mass transfer in bubble columns. CFD is widely recognized as a powerful tool for estimating critical hydrodynamic parameters but requires significant computational resources, time and expertise. These limitations restrict its practical use in hydrodynamic simulations that need real-time processing involving large-scale simulations of bubble columns. To overcome these limitations, CFD-DNN model is developed to predict the time averaged gas holdup and axial liquid velocity at various operating conditions. The DNN model was trained using CFD data that was produced for rectangular (with dimensions L=0.2 m, W=0.05 m, H=1.2 m) and cylindrical (with a diameter of 0.19 m) bubble columns. The data covers a range of operating conditions and various flow regimes. The superficial gas velocity for the rectangle column was selected at 1.33 and 7.3 mm/s, whereas for the cylindrical bubble column, it was fixed at 0.02 and 0.12 m/s. The CFD-DNN model was validated against the experimental and the CFD data from the literature. Further, the model was tested for new data that the CFD-DNN model has not seen with existing literature and showed good agreement with their data and it reflects the excellent generalization ability of the model. The proposed CFD-DNN approach improves current CFD models by providing shorter computing time, decreasing computational expenses, and reducing the expertise in CFD simulations. The accuracy of the developed CFD-DNN model was evaluated using different metrics for gas holdup and axial liquid velocity. For rectangular bubble columns, the model achieved MSE of 0.0001 for gas holdup and 0.0007 for axial liquid velocity. Similarly, for cylindrical bubble columns, the MSE values were 0.0009 for gas holdup and 0.0006 for axial liquid velocity.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100185"},"PeriodicalIF":3.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531726","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}

{"title":"Application of multi-objective neural network algorithm in industrial polymerization reactors for reducing energy cost and maximising productivity","authors":"Fakhrony Sholahudin Rohman ,&nbsp;Sharifah Rafidah Wan Alwi ,&nbsp;Dinie Muhammad ,&nbsp;Ashraf Azmi ,&nbsp;Zainuddin Abd Manan ,&nbsp;Jeng Shiun Lim ,&nbsp;Hong An Er ,&nbsp;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":"2024-09-07","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}