Digital Chemical Engineering最新文献

{"title":"BibMon: An open source Python package for process monitoring, soft sensing, and fault diagnosis","authors":"Afrânio Melo ,&nbsp;Tiago S.M. Lemos ,&nbsp;Rafael M. Soares ,&nbsp;Deris Spina ,&nbsp;Nayher Clavijo ,&nbsp;Luiz Felipe de O. Campos ,&nbsp;Maurício Melo Câmara ,&nbsp;Thiago Feital ,&nbsp;Thiago K. Anzai ,&nbsp;Pedro H. Thompson ,&nbsp;Fábio C. Diehl ,&nbsp;José Carlos Pinto","doi":"10.1016/j.dche.2024.100182","DOIUrl":"10.1016/j.dche.2024.100182","url":null,"abstract":"<div><p>This paper introduces BibMon, a Python package that provides predictive models for data-driven fault detection and diagnosis, soft sensing, and process condition monitoring. Key features include regression and reconstruction models, preprocessing pipelines, alarms, and visualization through control charts and diagnostic maps. BibMon also includes real and simulated datasets for benchmarking, comparative performance analysis of different models, and hyperparameter tuning. The package is designed to be highly extensible, allowing for easy integration of new models and methodologies through its object-oriented implementation. Currently, BibMon is in production at Petrobras, a major player in the energy industry, monitoring numerous industrial assets and enabling real-time detection and diagnosis of equipment and process faults. The software is open source and available at: <span><span>https://github.com/petrobras/bibmon</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100182"},"PeriodicalIF":3.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000449/pdfft?md5=2bad12662f6927eb2b46d33e008996a7&pid=1-s2.0-S2772508124000449-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151976","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":"Investigating amplitude amplification in optimization-based control for a continuous stirred tank reactor","authors":"Kip Nieman ,&nbsp;Helen Durand ,&nbsp;Saahil Patel ,&nbsp;Daniel Koch ,&nbsp;Paul M. Alsing","doi":"10.1016/j.dche.2024.100180","DOIUrl":"10.1016/j.dche.2024.100180","url":null,"abstract":"<div><div>Quantum computers, which utilize quantum states called ‘qubits’ to process information, are becoming of increased interest in a variety of fields because they have the potential to outperform classical computers in certain situations. However, many challenges and hurdles remain, including the development of quantum algorithms that offer a speedup and can be applied to practical problems (some quantum algorithms that offer a speedup may only be applicable in specific and sometimes contrived circumstances). Our previous works have studied the use of quantum computers and quantum algorithms for process control applications. Our prior work evaluated the use of a gate-based quantum amplitude amplification algorithm when applied to a model predictive control optimization problem, specifically evaluating a linear systems example. The results suggested that the algorithm may be suited for the specific linear problem studied (meaning that there is a high probability of obtaining the desired result when measuring the quantum state after the algorithm has been applied). However, the results cannot be generalized to a wider class of systems or to systems containing nonlinearities. To begin to gain an understanding of how the amplitude amplification algorithm might be generalized, this work evaluates the use of the algorithm for the optimization-based control of a nonlinear dynamic system (specifically, a continuous stirred tank reactor). We seek to understand aspects of the problem, such as the development of a solution space, the effects of changing process and control parameters, and the operation of the amplitude amplification algorithm itself. The results demonstrate the challenges that still exist, and demonstrate a method involving inverse sampling transformations to potentially aid in addressing these issues.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100180"},"PeriodicalIF":3.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323100","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":"Predicting soot formation in fossil fuels: A comparative study of regression and machine learning models","authors":"Ridhwan Lawal ,&nbsp;Wasif Farooq ,&nbsp;Abdulazeez Abdulraheem ,&nbsp;Abdul Gani Abdul Jameel","doi":"10.1016/j.dche.2024.100172","DOIUrl":"10.1016/j.dche.2024.100172","url":null,"abstract":"<div><p>The incomplete combustion of fossil fuels results in the emission of soot, a carbonaceous, solid fine powder that causes harm to human health and the environment. This study compares multiple linear regression (MLR) with three different machine learning (ML) models for predicting the threshold sooting index (TSI), a commonly employed index for measuring the sooting propensity of fuels. The dataset used for model development consists of experimental TSI data for 342 fuels, including various chemical classes, including oxygenated components like ethers and alcohols. Ten input features were employed, comprising eight functionalities, molecular weight, and the branching index (BI). These parameters used as input features have been demonstrated to affect fuels' physical and thermochemical properties. The ML models employed in this study are support vector regression with Nu parameter (NuSVR), extra trees regression (ETR), and extreme gradient boosting regression (XGBR). The models were trained, validated, and tested using randomly split datasets, with 56 % for training, 14 % for validation, and 30 % for testing. The accuracy of the MLR, NuSVR, ETR, and XGBR models for the entire dataset was 91 %, 96 %, 98 %, and 96 %, respectively. The mean absolute errors (MAE) of prediction were 3.4, 0.022, 0.011, and 0.028 for MLR, NuSVR, ETR, and XGBR respectively. These results highlight the effectiveness of the ML models in making predictions, with error levels similar to the uncertainties observed in experimental measurements. The developed ML models have been validated to ensure generalizability and can be used to predict petroleum fuels' TSI.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100172"},"PeriodicalIF":3.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000346/pdfft?md5=cc7397098bfb4ba34202a20ec0a0dd60&pid=1-s2.0-S2772508124000346-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049954","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":"Parallelizing process model integration for model predictive control through oracle design and analysis for a Grover’s algorithm-inspired optimization strategy","authors":"Kip Nieman ,&nbsp;Helen Durand ,&nbsp;Saahil Patel ,&nbsp;Daniel Koch ,&nbsp;Paul M. Alsing","doi":"10.1016/j.dche.2024.100179","DOIUrl":"10.1016/j.dche.2024.100179","url":null,"abstract":"&lt;div&gt;&lt;p&gt;In model predictive control (MPC), a process dynamic model is utilized to make predictions of the value of the objective function and constraints throughout a prediction horizon. In one method of solving this problem, the time required to find the optimal values of the decision variables depends on the time required to perform the arithmetic operations involved in computing the model predictions. Methods for attempting to reduce the computation time of an MPC could then include developing approximate (reduced-order or data-driven) models for a system that take less time to solve, or to parallelize the computations using, for example, multiple cores or CPU’s. However, an observation in all of these cases is that the values of the process states across the prediction horizon are not the values returned by the optimization problem; the manipulated input trajectory is the desired decision variable. An optimization strategy that cannot explicitly return the process states but can generate some representation of them that then leads to computation of the desired process input would thus be suitable for MPC. Quantum computers achieve their parallelism through creating values that cannot all be returned. They can then operate on this set of values to return a number that is meaningful with respect to that set of values that could not all be returned. Motivated by this, we wish to investigate an idea for utilizing quantum parallelism in developing a representation of an objective function that depends on the solution of a process dynamic model, but then only returning the control actions that minimize the objective function value dependent on those solutions. To do this, several steps are necessary. The first is to locate a quantum algorithm which has the desired characteristics for achieving the goals. In this work, we perform these steps using an amplitude amplification strategy based on Grover’s algorithm on a quantum computer. The second is to analyze the algorithm with respect to its ability to translate across problems in the MPC domain, with respect to both its ability to handle nonlinear systems and to handle a variety of different structures of the set of all possible objective function values given the allowable values of the decision variables. We thus evaluate the benefits and limitations of the algorithm from this perspective. We do not wish to imply that this algorithm is more computationally-tractable for use with MPC than classical optimization techniques traditionally applied in an MPC context. Rather, we wish to understand the manner in which such an algorithm would be designed and when it is appropriate for MPC problems (in the sense of returning the correct answers to the optimization problem), as a step toward better understanding the interactions of the quantum properties of quantum algorithms with control goals. We also see this as forming an important first step in algorithm design/analysis, which can then translate to futu","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100179"},"PeriodicalIF":3.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000413/pdfft?md5=39ec1933d93357d58eafb0ba6a58aa2f&pid=1-s2.0-S2772508124000413-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164566","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":"CFD modeling of spray drying of fresh whey: Influence of inlet air temperature on drying, fluid dynamics, and performance indicators","authors":"Jamille Coelho Coimbra ,&nbsp;Letícia Campos Lopes ,&nbsp;Weskley da Silva Cotrim ,&nbsp;Diego Martinez Prata","doi":"10.1016/j.dche.2024.100178","DOIUrl":"10.1016/j.dche.2024.100178","url":null,"abstract":"<div><p>Whey is a very perishable food and a potent source of protein. It might be more commercialized through the process of spray drying, which would enhance its shelf life. No CFD computational models applied to the drying of fresh sweet whey have been reported in the literature to investigate transport phenomena in spray dryers and to predict crucial design parameters such as deposition, powder recovery, and drying efficiency. Using an Eulerian-Langragean technique, the behavior of multiphase flow as well as heat and mass transfer were investigated. The influence of the inlet air temperature was evaluated in relation to the velocity profiles of the continuous and discrete phases, the residence time distribution, the particle diameter formed, the air temperature near the injection zone, the particle temperature, and the mass fraction of evaporated water. Furthermore, performance parameters such as powder recovery, particle deposition, and drying efficiency were projected for varied air inlet temperatures. The velocity, residence time, and particle size distribution patterns were similar for the different air inlet temperatures. Greater variations might be noticed in the injection zone, especially in the temperature and mass fraction profiles. The lowest drying temperature resulted in the lowest particle deposition and the best thermal efficiency, making it the optimal process condition. This investigation can serve as a benchmark for the design of optimized spray dryers with greater thermal efficiency and yield to produce powdered whey.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100178"},"PeriodicalIF":3.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000401/pdfft?md5=2cef0233c09be489b4fbedb8471cf459&pid=1-s2.0-S2772508124000401-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142058526","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":"Modelling and verification of the nickel electroforming process of a mechanical vane fit for Industry 4.0","authors":"Eleni Andreou ,&nbsp;Sudipta Roy","doi":"10.1016/j.dche.2024.100177","DOIUrl":"10.1016/j.dche.2024.100177","url":null,"abstract":"<div><p>In previous studies, the comprehensive scaling-up of nickel electroforming on a lab-scale rotating disk electrode (RDE) suggested that secondary current distribution could adequately simulate such a forming process. In this work, the use of a 3-D, time-dependent, secondary current distribution model, developed in COMSOL Multiphysics®, was examined to validate the nickel electroforming of an industrial mechanical vane, a low-tolerance part with a demanding thickness profile of great interest to the aerospace industry. A set of experiments were carried out in an industrial pilot tank with computations showing that the model can satisfactorily predict the experimental findings. In addition, these experiments revealed that the local applied current density was related to the surface appearance (shiny <em>vs</em> matt) of the electroform.</p><p>Simulations of the process at applied current densities <span><math><mrow><mo>≤</mo><mn>5</mn><mspace></mspace><mi>A</mi><mo>/</mo><mi>d</mi><msup><mrow><mi>m</mi></mrow><mn>2</mn></msup></mrow></math></span> satisfactorily predicted the experimentally observed thickness distribution while, simulations of the process at applied current densities <span><math><mrow><mo>≥</mo><mn>5</mn><mspace></mspace><mi>A</mi><mo>/</mo><mi>d</mi><msup><mrow><mi>m</mi></mrow><mn>2</mn></msup></mrow></math></span> underpredicted the experimentally achieved thicknesses. Nevertheless, it is proposed that the model can be used for either quantitative or qualitative studies, respectively, depending on the required operating current density on a case-by-case basis. Scanning electron microscopy was used to determine the microstructure of the electroforms and determine the purity of nickel (<em>i.e.</em>, if nickel oxide is formed), with imaging suggesting that pyramid-shaped nickel particles evolve during deposition. Another interesting observation revealed a periodicity in the deposit's growth mechanism which leads to “necklace”-like deposit layers at the areas where the electroforms presented the highest thickness.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100177"},"PeriodicalIF":3.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000395/pdfft?md5=2df1d77a423523fe3d394d7872e34639&pid=1-s2.0-S2772508124000395-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142075849","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":"Efficient chemical equilibria calculation by artificial neural networks for ammonia cracking and synthesis","authors":"Hannes Stagge ,&nbsp;Theresa Kunz ,&nbsp;Sina Ramsayer,&nbsp;Robert Güttel","doi":"10.1016/j.dche.2024.100176","DOIUrl":"10.1016/j.dche.2024.100176","url":null,"abstract":"<div><p>The calculation of chemical equilibria in detailed reactor simulations frequently requires elaborate numerical solution of the governing equations in an iterative way, which is often computationally expensive and can significantly increase the overall computation time. In order to reduce these computational costs, we introduce a ready-to-use tool, ^ANNH₃, for calculation of equilibrium composition for synthesis and cracking of ammonia based on a neural network. This tool provides excellent agreement with the conventional approach in the range of 135–1000 °C and 1–100 bar and is ca. 100 times faster than conventional stoichiometry-based concepts by replacing the iterative solution process with neural network inference. While speed-up is significant even for the relatively simple case of ammonia synthesis and decomposition, we expect an even higher performance gain for the equilibrium calculation in reaction systems where more components and multiple reactions are involved.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100176"},"PeriodicalIF":3.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000383/pdfft?md5=0496e96ed6bb816a7f908f08d67c84db&pid=1-s2.0-S2772508124000383-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049953","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":"Flow regime transition maps and pressure loss prediction of gas, oil and water three-phase flow in the vertical riser downstream 90° bend using data driven approach","authors":"Muhammad Waqas Yaqub ,&nbsp;Rajashekhar Pendyala","doi":"10.1016/j.dche.2024.100174","DOIUrl":"10.1016/j.dche.2024.100174","url":null,"abstract":"<div><p>The simultaneous flow of gas, oil &amp; water is frequently encountered in pipelines during upstream petroleum operations. The multiphase flow results in different types of flow patterns based on the flow rates of fluids, physical properties and geometry of the flow domain. The flow behavior is characterized based on the governing flow patterns. Hence, the information about the flow patterns, regime maps and resulting pressure loss are important for multiphase flow system design and optimization. The current work is focused on construction of gas, oil and water, three-phase flow regime maps and developing pressure loss prediction correlations for the flow through vertical riser downstream 90° bend. The pipe internal diameter (ID) is 6 inch and the bending radius to pipe diameter ratio is 1. The observed gas-liquid flow patterns are slug, churn, and semi-annular churn flow at the given range of superficial velocities of fluids. The flow pattern data has been used to construct flow regime maps to analyze the variation in flow patterns with flow rates of fluids and compared with the available works in the literature. In addition, the change in pressure loss with respect to flow patterns has been analyzed. Previous models are used for the prediction of pressure loss. However, according to the assessment, the models underpredicted the pressure loss. Based on three-phase pressure loss data, multiple linear regression analysis has been carried out to propose new correlations for pressure loss prediction. Comparison of the calculated and experimental data showed good agreement between the results. The knowledge of flow regime variation and pressure loss correlations can help flow assurance engineers in designing and optimization of multiphase flow systems.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100174"},"PeriodicalIF":3.0,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277250812400036X/pdfft?md5=b0e832652a64a6dfb575aa6b0370bd74&pid=1-s2.0-S277250812400036X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985479","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":"Optimized structure design for binary particle mixing in rotating drums using a combined DEM and gaussian process-based model","authors":"Leqi Lin ,&nbsp;Xin Zhang ,&nbsp;Mingzhe Yu ,&nbsp;Iqbal M Mujtaba ,&nbsp;Xizhong Chen","doi":"10.1016/j.dche.2024.100175","DOIUrl":"10.1016/j.dche.2024.100175","url":null,"abstract":"<div><p>Particle mixing is a crucial operation in various industrial production processes. However, phenomena like segregation or local accumulation can arise, especially when particles differ in properties like radius and density. Numerical simulation of particles using Discrete Element Method (DEM) allows for the manipulation of control variables in batches, generating a large amount of data and facilitating quantitative research. In this study, the mixing behaviors of binary particles in rotating drums are systematically investigated. The DEM model is first validated with experimental data and then rotating drums with varying obstacles, rotation speeds, particle radii, and densities are simulated. Moreover, a Gaussian process-based optimization is conducted by correlating Lacey mixing index (MI) and parameterized shape of obstacle to find the optimized mixing condition. Experimental validations are further performed on the optimized condition to verify the design. It is shown that this integrated approach holds significant potential for enhancing the efficiency, effectiveness of industrial mixing processes and the consideration of energy consumption when balancing the mixing efficiency and optimal rotating speed.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100175"},"PeriodicalIF":3.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000371/pdfft?md5=aadaca505a7394a183b59951d9944055&pid=1-s2.0-S2772508124000371-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951970","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":"Machine learning-based predictive control of an electrically-heated steam methane reforming process","authors":"Yifei Wang ,&nbsp;Xiaodong Cui ,&nbsp;Dominic Peters ,&nbsp;Berkay Çıtmacı ,&nbsp;Aisha Alnajdi ,&nbsp;Carlos G. Morales-Guio ,&nbsp;Panagiotis D. Christofides","doi":"10.1016/j.dche.2024.100173","DOIUrl":"10.1016/j.dche.2024.100173","url":null,"abstract":"<div><p>Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional–integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.</p></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"12 ","pages":"Article 100173"},"PeriodicalIF":3.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000358/pdfft?md5=a8e02c83c8c26b294c07f20e620613f3&pid=1-s2.0-S2772508124000358-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853429","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}