Digital Chemical Engineering最新文献

{"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}

{"title":"Equilibrium modelling of steam gasification of PKS system and CO2 sorption using CaO: A digitalized parametric and techno-economic analysis","authors":"Zakir Khan ,&nbsp;Muhammad Shahbaz ,&nbsp;Syed Ali Ammar Taqvi ,&nbsp;Ahmed AlNouss ,&nbsp;Tareq Al-Ansari ,&nbsp;Usama Ahmed","doi":"10.1016/j.dche.2024.100184","DOIUrl":"10.1016/j.dche.2024.100184","url":null,"abstract":"<div><div>The conversion of palm oil waste into energy can complement the energy mix with renewable energy and bring economic benefits to the palm oil industry. A process simulation model for palm kernel shell (PKS) steam gasification for H₂-enriched syngas with the integration of CO₂ capturing using CaO has been investigated using Aspen Plus V10®. Techno-economic and energy analyses have also been conducted to identify energy-saving opportunities for commercialization. The effect of process variables, including reactor temperature (600–800 °C), Steam/PKS ratio (0.5–2 wt/wt), and CaO/PKS ratio (0–1.5 wt/wt), have been determined, with the predicted results compared to the reported experimental data. H₂ concentration has been increased with 76–78 vol% with the temperature elevation from 650 to 750 °C. Additionally, a substantial increase in H₂ content from 68 to 72vol% was observed when the steam flow rate was increased from 0.5 to 1.5. Conversely, the CO₂ concentration decreased from 25 to 8vol% as the adsorbent ratio was raised from 0.5 to 1.5. The techno-economic analysis showed that the capital investment is $4.11 million, and the operating cost is $3.89 million per year, which is also very high due to the high raw material costs. In the case of energy, saving that 3.03 and 1.513 Gcal/hr can be saved in terms of utilities and gas cooling that economized the process which shows that utilization of these in heat exchange networks can significantly reduce costs, with up to 78 % savings in heat exchanger capital and 35 % in total energy consumption. The energy potential could be harnessed through the digitalization of the process.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"13 ","pages":"Article 100184"},"PeriodicalIF":3.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772508124000462/pdfft?md5=7696264059ba3a9a724daa333bcc2bb6&pid=1-s2.0-S2772508124000462-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311197","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":"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}