Digital Chemical Engineering最新文献

{"title":"Case hacks in action: Examples from a case study on green chemistry in education for sustainable development","authors":"Per Fors ,&nbsp;Thomas Taro Lennerfors ,&nbsp;Jonathan Woodward","doi":"10.1016/j.dche.2023.100129","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100129","url":null,"abstract":"<div><p>This paper aims to outline an approach for case-based chemistry and chemical engineering education for sustainability. Education for Sustainability is assumed to offer a holistic approach to equip students with the knowledge, skills, values, and attitudes needed to contribute to a more sustainable society in their future careers. While Case-Based Education traditionally focuses on disciplinary learning in simulated settings, it can also effectively teach essential sustainability-related skills like integrated problem-solving, critical thinking, and systems thinking. The approach we propose is “case hacking”, which should be understood as utilizing existing business cases while incorporating supplementary resources to align the assignment with intended learning objectives. This expansion of the cases involves, among other things, introducing additional questions and assignments, perspectives from stakeholders previously unexplored in the original case, and the integration of recent research articles from relevant fields. We advocate for the use of case hacking when educators want to harness the educational benefits of Case-Based Education while emphasizing the complexity of sustainability-related challenges faced by industrial companies today. As an illustrative example, we demonstrate the process of hacking a case related to Green Chemistry in the pharmaceutical industry, highlighting specific challenges for chemistry and chemical engineering education. We hope this example will inspire educators in these disciplinary contexts to engage with the case hacking approach as they navigate the complex terrain of sustainability.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"9 ","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49714948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Water desalination using PSO-ANN techniques: A critical review","authors":"Rajesh Mahadeva ,&nbsp;Mahendra Kumar ,&nbsp;Vishu Gupta ,&nbsp;Gaurav Manik ,&nbsp;Vaibhav Gupta ,&nbsp;Janaka Alawatugoda ,&nbsp;Harshit Manik ,&nbsp;Shashikant P. Patole ,&nbsp;Vinay Gupta","doi":"10.1016/j.dche.2023.100128","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100128","url":null,"abstract":"<div><p>Water is a natural and essential resource for humans, animals, and plants to persist. However, only ⁓2.5 % of the freshwater resources are available, while the remaining ⁓97.5 % is saline water, which is unsuitable for humanity. According to the WHO, water scarcity will worsen by 2050. As a result, numerous researchers, scientists, and engineers are working in this field to improve water resources with advanced treatment technologies. Aside from the multiple water resources, desalination is critical in converting saline water to fresh water. In line with a recent update from the International Desalination Association (IDA, Reuse Handbook 2022–23), approximately ⁓22,757 desalination plants are operating worldwide, providing ⁓107.95 million cubic meters of freshwater per day (m³/day). Furthermore, in this digital age, artificial intelligence (AI) techniques, such as gray wolf optimization (GWO), sine cosine algorithm (SCA), artificial neural networks (ANN), multi-verse optimizer (MVO), fuzzy logic systems (FLS), moth flame optimizer (MFO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA) and genetic algorithms (GA), are playing a vital role and capable of deep analysis of real-time desalination plant for saving time, energy, human efforts, and money. This study focuses on the critical review and various aspects of current-age PSO-ANN techniques for desalination plants. In this regard, recent datasets of the Web of Science (WoS), provided by Clarivate Analytics, state that about &gt;54,856 records (1965–2023) of desalination and around &gt; 180 records (2008–2023) of PSO-ANN techniques are available globally. These records involve research articles, reviews, proceedings, letters, books, chapters, and editorial materials. Finally, this review article is specific and analyzes the various perspectives of PSO-ANN techniques in the water desalination process, promoting plant engineers and researchers to improve plant performance with minimum effort and time.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"9 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49714707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complementary role of large language models in educating undergraduate design of distillation column: Methodology development","authors":"Zong Yang Kong ,&nbsp;Vincentius Surya Kurnia Adi ,&nbsp;Juan Gabriel Segovia-Hernández ,&nbsp;Jaka Sunarso","doi":"10.1016/j.dche.2023.100126","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100126","url":null,"abstract":"<div><p>This paper explores the integration of large language models (LLMs), such as ChatGPT, in chemical engineering education, departing from conventional practices that may not be universally accepted. While there is ongoing debate surrounding the acceptance of LLMs, driven by concerns over computational instability and potential inconsistencies, their inevitability in shaping our communication and interaction with technology cannot be ignored. As educators, we are positioned to play a vital role in guiding students toward the responsible, effective, and synergetic use of LLMs. Focusing specifically on distillation column design in undergraduate mass-transfer courses, this study demonstrates how ChatGPT can be utilized as an auxiliary tool to create interactive learning environments and simulate real-world engineering thinking processes. It emphasizes the need for students to develop critical thinking skills and a thorough understanding of LLM principles, taking responsibility for their use and creations. While ChatGPT should not be solely relied upon, its integration with fundamental principles of chemical engineering is crucial. The effectiveness and limitations of ChatGPT are exemplified through two case studies, showcasing the importance of manual calculations and established simulation software as primary tools for guiding and validating engineering results and analyses. This paper also addresses the pedagogical implications of integrating LLMs into mass transfer courses, encompassing curriculum integration, facilitation, guidance, and ethical considerations. Recommendations are provided for incorporating LLMs effectively into the curriculum. Overall, this study contributes to the advancement of chemical engineering education by examining the benefits and limitations of LLMs as educational aids in the design process.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"9 ","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49731754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twin assisted decision support system for quality regulation and leak localization task in large-scale water distribution networks","authors":"Parth Brahmbhatt ,&nbsp;Abhilasha Maheshwari ,&nbsp;Ravindra D. Gudi","doi":"10.1016/j.dche.2023.100127","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100127","url":null,"abstract":"<div><p>Effective water resource management is essential in large metropolitan cities. Digital Twins (DT), supported by IIoT and machine learning technologies, provide opportunities for real-time prediction and optimization for effective decision-making in water distribution systems. A framework for the digital twin of the Water Distribution Network (WDN) is developed in this paper to achieve higher operational efficiency using ‘<em>WNTR</em>’, the Python-based library of EPANET. All computational experiments and methods were validated on the benchmark hydraulic C-TOWN network (Ostfeld et al., 2011). The hydraulic parameters and quality parameters of the DT model for the water network were calibrated using the Differential Evolution (DE) algorithm. The calibrated DT served as a real-time proxy to generate simulation data, which is used for two different applications in large-scale water networks: (i) Disinfectant dosage regulation task using booster stations and (ii) pipe leakage localization task. The calibrated DT was utilized to estimate the optimal disinfectant dosing rates, ensuring water quality control within an acceptable range using optimization. The results highlight the effectiveness of the neural network and real-time optimization strategy to achieve the optimal dosing rate. For the leakage localization task, the Graph Convolution Networks (GCN) based neural network trained on the DT was found to predict leakage location very accurately.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"9 ","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49731757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and optimal tuning of fraction order controller for multiple stage evaporator system","authors":"Smitarani Pati ,&nbsp;Nikhil Pachori ,&nbsp;Gaurav Manik ,&nbsp;Om Prakash Verma","doi":"10.1016/j.dche.2023.100125","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100125","url":null,"abstract":"<div><p>The tight control of the process parameters through appropriate tuning of controllers is an art that imperatively employed to various process industries. Most of these industries are influenced by the nonlinearity that occurred due to the input parameter variation and presence of disturbances. The aim of this work is to investigate the nonlinear dynamics of a paper industry based energy intensive unit named Multiple Stage Evaporator (MSE) in presence of different Energy Reduction Schemes. MSE is used to concentrate the weak Black Liquor (BL), a biomass based byproduct. Hence, to extract the bioenergy from the BL, the quality of the product liquor needs to be appropriately controlled. The quality of BL is measured by two process parameters, product concentration and temperature. Hence, in this work, an intelligent controller Fraction Order Proportional-Integral-Derivative controller has been studied and employed to resolve the servo and the regulatory problem occurred during the process. A state-of-art metaheuristic approach, Black Widow Optimization Algorithm has been proposed here to tune the controller parameters and compared with another optimization approaches named Water Cycle Algorithm. The simulated result demonstrates the usefulness of the proposed strategy and confirm the performance improvement for the process parameters. To enlighten the advantages of the proposed control scheme, a comparative analysis have also been performed with conventional PID, 2-DOF-PID and FOPID controllers.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"9 ","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49714999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cloud-based virtual flow metering system powered by a hybrid physics-data approach for water production monitoring in an offshore gas field","authors":"Rafael H. Nemoto,&nbsp;Roberto Ibarra,&nbsp;Gunnar Staff,&nbsp;Anvar Akhiiartdinov,&nbsp;Daniel Brett,&nbsp;Peder Dalby,&nbsp;Simone Casolo,&nbsp;Andris Piebalgs","doi":"10.1016/j.dche.2023.100124","DOIUrl":"https://doi.org/10.1016/j.dche.2023.100124","url":null,"abstract":"<div><p>This work presents a cloud-based Virtual Flow Metering (VFM) system powered by a hybrid physics-data approach to estimate the water production per well in a gas field. This hybrid approach, which allows accurate calculations near real-time conditions, is based on the description of the flow through the wellbore using physics-based models pertaining to gas-liquid flows with high gas volume fraction. A data-driven approach is implemented to tune the flow model using well test data. This implementation accounts for changes in the well performance and increase in water production, resulting in a self-calibrating solution. This means that the model will remain accurate and relevant as production and well conditions change. Results from the VFM show good agreement with the well test data for steady-state conditions. The VFM calculations are performed remotely using a cloud-based DataOps platform where results are also stored. This allows continuous access to live sensor data to be used as input to other applications or visualized through a web interface. The VFM system uses a set of readily available sensors installed in the wells. Thus, it represents cost reduction in both capital and operating expenditures when compared to the installation of multiphase flow meters or separators.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"9 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49731751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach","authors":"Xin Yee Tai ,&nbsp;Lei Xing ,&nbsp;Yue Zhang ,&nbsp;Qian Fu ,&nbsp;Oliver Fisher ,&nbsp;Steve D.R. Christie ,&nbsp;Jin Xuan","doi":"10.1016/j.dche.2023.100123","DOIUrl":"10.1016/j.dche.2023.100123","url":null,"abstract":"<div><p>The increasing demand for net zero solutions has prompted the exploration of electrochemical CO₂ reduction reaction (eCO₂RR) systems powered by renewable energy sources. Here, we present a comprehensive AI-enabled framework for the adaptive optimisation of the dynamic eCO₂RR processes in response to the intermittent renewable energy supply. The framework includes (1). a Bi-LSTM (bidirectional long-short-term memory) to predict the meteorological data for renewable energy input; (2). a deep learning surrogate model to predict the eCO₂RR process performance; and (3). a NSGA-II algorithm for multi-objective optimisation, targeting the trade-off of the single-pass Faraday efficiency (FE), product yield (PY) and conversion. The framework seamlessly integrates the three different AI modules, enabling adaptive optimisation of the eCO₂RR system composed of electrolyser stacks and renewable energy sources, and providing insights into system's performance and feasibility under real-world conditions.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"9 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46134740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microcontrollers programming for control and automation in undergraduate biotechnology engineering education","authors":"M.A. Márquez-Vera ,&nbsp;M. Martínez-Quezada ,&nbsp;R. Calderón-Suárez ,&nbsp;A. Rodríguez ,&nbsp;R.M. Ortega-Mendoza","doi":"10.1016/j.dche.2023.100122","DOIUrl":"10.1016/j.dche.2023.100122","url":null,"abstract":"<div><p>This paper presents the utilization of the ESP32 microcontroller as a teaching tool for signal acquisition, processing, and control theory in biotechnological engineering. The ESP32 microcontroller, equipped with Bluetooth and WiFi capabilities, offers an affordable and versatile solution for educational purposes. By leveraging the Arduino© software, students can easily learn microcontroller programming and utilize various peripherals such as sensors and actuators. Several practical exercises related to process control have been conducted using this microcontroller. Additionally, remote process monitoring and control are enabled through integration with a database. Furthermore, concepts of artificial intelligence are explored using the Edge Impulse platform to obtain an artificial neural network that can be downloaded onto the ESP32. Positive feedback from students highlights the effectiveness and engagement of utilizing these microcontrollers, and the integration of internet connectivity enhances the overall learning experience.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"9 ","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42399842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online monitoring of spatial-temporal distribution of harmful gases during advanced oxidation of NO by convolutional networks and gated recurrent units","authors":"Yue Liu,&nbsp;Xiangxiang Gao,&nbsp;Zhongyu Hou","doi":"10.1016/j.dche.2023.100110","DOIUrl":"10.1016/j.dche.2023.100110","url":null,"abstract":"<div><p>Online monitoring of the spatial-temporal distribution of harmful gases has always been a complex problem in the environmental field. This paper proposes a novel mathematical method for online monitoring of the spatial-temporal distribution of reactants by machine learning, which can help to remove harmful gases efficiently. In this model, we take the advanced oxidation of NO as an example to evaluate the model performance. The spatial features were extracted by CNN, and GRU extracted the temporal features in the sequence of spatial features. Five physical field variables (mass fraction of ozone, velocity, temperature, the wind direction of the horizontal plane, and the wind direction of the vertical plane) were put into the network to predict NO's spatial-temporal mass fraction distribution. Furthermore, the impact of sampling time interval on monitoring performance was also evaluated. The results show that both the instantaneous and continuous CFD (Computational Fluid Mechanics) and predicted values show high consistency, which indicates that the model can online monitor the spatial-temporal distribution of reactants successfully. In addition, the most suitable sampling time interval is 0.5 s, with low training error (RMSE=0.06 and nRMSE=0.3) and high relation coefficient (r=0.99), which shows the model has great perceived and predicted performance under this condition.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"8 ","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42429632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Convolutional Neural Network-based gradient boosting framework for prediction of the band gap of photo-active catalysts","authors":"Avan Kumar ,&nbsp;Sreedevi Upadhyayula ,&nbsp;Hariprasad Kodamana","doi":"10.1016/j.dche.2023.100109","DOIUrl":"10.1016/j.dche.2023.100109","url":null,"abstract":"<div><p>A recent trend in chemical synthesis is photo-catalysis, which uses photo-active catalyst materials that are semiconductor materials. A well-known electronic property of semiconducting materials is the band gap. A photo-catalyst’s desired band gap range is between 1.5 eV and 6.2 eV. A rational design and synthesis of photo-active catalysts require knowledge of the band gap as an initial screening parameter. Herein, we propose an integrated deep learning-based framework to classify the photo-active catalysts and predict their band gap using compositional features. To this extent, we have utilized the dataset extracted from the “catalyst hub” site by web scraping with the help of a Python script. Extensive data cleaning and pre-processing are done to make input data amenable for training the models. Also, more valuable features are made using two methods: (a) one hot-encoding and (b) calculating the mean of the embeddings of catalysts computed by Mat2Vec, a pre-trained transformer-based model. With the help of this generated feature set, we have proposed a two-stage deep-learning framework for classification and regression tasks. In the first stage, a 2D-Convolutional Neural Net (CNN)-based classifier is used to classify whether a catalyst belongs to the photo-active catalyst class. After the first stage screening, in the second stage, we use a 1D-VGG-based gradient boosting framework to predict the band gap of the photo-active catalyst only using compositional features as inputs. 2D-CNN for the classification task has an accuracy of 0.903 and 0.886 for the train and test datasets, respectively. Further, the proposed integrated model that uses 1D-Convolutional layers of VGG followed by the XGBoostRegressor has a test <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.750, much higher than baseline models reported in the literature.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"8 ","pages":"Article 100109"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44765634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}