Process Industries Research Articles

Simulation Model Exchange in Process Industry: Requirements, Solutions, and Open Challenges

AbstractSimulation models are crucial for various applications across the lifecycle of process plants, such as plant engineering. As the industry moves toward modularization, plant components are increasingly supplied by manufacturers who design these modular units, often called package units or process equipment assemblies. Consequently, critical knowledge about their behavior resides with the manufacturers. This paper outlines the requirements for simulation model exchange to share this behavioral knowledge. It reviews solutions and highlights challenges based on literature and case studies. Key elements include metadata, open standards, IP protection, and quality assurance. Solutions, like the asset administration shell (AAS), CAPE‐OPEN, the Functional Mock‐up Interface (FMI), encrypted models, and quality models, are discussed. Remaining gaps are highlighted.

Automatic Production Planning and Scheduling of Size

Automatic production planning and scheduling are critical for optimizing manufacturing processes in modern industries. This approach integrates advanced algorithms, machine learning, and real-time data analysis to streamline resource allocation, minimize production bottlenecks, and enhance overall efficiency. By automating traditionally manual planning tasks, the system improves accuracy and responsiveness to dynamic production demands. Challenges such as implementation costs, integration with existing systems, and data reliability are addressed through scalable architectures and robust predictive models. This study investigates innovative methodologies to achieve seamless coordination, examining their impact on productivity, cost reduction, and adaptability in competitive markets Key Words: optics, photonics, light, lasers, templates, journals

Solvometallurgical Properties of Choline Chloride-Based Deep Eutectic Solvents for Copper Extraction from Chalcopyrite: Optimization and Analysis

This paper focuses on the solvometallurgical properties of choline chloride-based deep eutectic solvents for copper extraction from chalcopyrite concentrate. The study, conducted with scientific rigor, utilized the response surface methodology to optimize the extraction process and investigate the effects of the temperature and contact time on the copper recovery efficiency. The results showed that the ChCl-EG-Ox solvent at 80 °C and 48 h produced the highest copper recovery rate, exceeding 76%. This underscores the potential of deep eutectic solvents for sustainable metal extraction. Kinetic studies revealed the influence of temperature on dissolution kinetics, with higher temperatures leading to faster reaction rates. The mineralogical analysis demonstrated the changes in the chalcopyrite concentrate after dissolution, while spectroscopy and mass spectrometry highlighted the esterification reactions in the solvent. The study also examined the effects of adding water and heating on the solvent’s behavior, providing insights into the chemical interactions and structural changes. Ultimately, the research demonstrated that ChCl-based deep eutectic solvents present a promising avenue for environmentally friendly and efficient copper extraction processes in the metallurgical industry.

Industry 4.0 and the Importance of an Ecosystem‐Based Approach: An Empirical Investigation Based on a Mixed‐Method Analysis

ABSTRACTThis paper will analyze the role of the innovation ecosystem (IES) in the industry implementation process of Industry 4.0 (I4.0) at company level. Based on a mixed‐method analysis using econometric models and content analysis, we have investigated the implementation process of industry 4.0‐enabling technologies of 123 manufacturing companies in the Hauts‐de‐France region of France. We have showed that the implementation process of industry 4.0‐enabling technologies is facilitated by the complexities of relationships between actors, complementarities among actors, particularly between start‐ups and others ecosystem stakeholders, and the role of regional policies (innovation support). Our quantitative results confirm that complex relationships with heterogenous actors within an IES positively affect the implementation process of Industry 4.0 at company level. While our qualitative analysis has showed that business‐to‐business relationships were decisive, our quantitative analysis has gone further and has showed that the more a given company develops diverse forms of relationships with other companies within the IES, the higher the level of I4.0 implementation‐enabling technologies there are in the company. Both qualitative and quantitative results demonstrate the importance of university‐industry linkage and of business incubators for the I4.0 implementation process, in enabling technologies at company level. A further result that completes our econometric study is the clear identification by company managers of the key role played by collaborative innovation projects and by start‐ups as providers of new solutions in fostering their I4.0 technological development. Therefore, our research calls for an ecosystem‐based approach to fostering I4.0 implementation at company level.

Water Treatment Technologies: Development of a Test Bench for Optimizing Flocculation-Thickening Processes in Laboratory Applications

This study introduces an automated test bench designed to optimize flocculation-thickening processes in the wastewater treatment industry. Addressing current challenges in operational efficiency and cost reduction, the test bench employs Model-Based Systems Engineering (MBSE) principles, leveraging SysML modeling within the CESAM framework. By integrating advanced technologies, including PLC programming and a closed-loop control system, this bench provides precise and efficient testing under varying operational conditions. Economic implications are explored, demonstrating the cost-effectiveness of optimized flocculation processes, which reduce chemical use and operational expenditures while enhancing water clarity and sludge management. The system’s 3D modeling enables detailed simulations, aiding in both research and pedagogical applications. This platform highlights the potential of MBSE in creating scalable, robust solutions that contribute to sustainable water management.

Tailoring Metal-Organic Frameworks for One-Step Separation of Alkane/Alkene/Alkyne Mixtures.

The purification of polymer-grade (>99.9%) olefins (mostly C2 and C3) represents a significant yet challenging process in petrochemical industry. The commonly employed method for hydrocarbon separation involves heat-driven distillations. Adsorptive separation based on porous solids offer the potential to purify olefins under ambient conditions, rendering considerable energy and environmental benefits. In particular, one-step purification of alkenes through selective adsorption of their corresponding alkanes and alkynes stands out as a promising technique. Notably, metal-organic frameworks (MOFs), possessing finely tunable pore structures (pore size/shape/internal chemical environment), hold great potential in this regard. In this review article, we outline recent progresses in the development of MOFs for one-step adsorptive purification of alkenes from alkane/alkene/alkyne ternary mixtures, with an emphasis on the rational regulation of pore structures for desired separation.

Integrated Risk Framework (IRF)—Interconnection of the Ishikawa Diagram with the Enhanced HACCP System in Risk Assessment for the Sustainable Food Industry

This paper presents a new risk assessment methodology called the Integrated Risk Framework (IRF) through the application of Ishikawa diagrams combined with the enhanced Hazard Analysis and Critical Control Point (HACCP) system. This risk investigation technique aims to ensure a significantly higher level of quality, safety, and sustainability in food products by using improved classical methods with strong intercorrelation capabilities. The methodology proposes expanding the typology of basic physical, chemical, and biological risks outlined by the ISO 22000 Food Safety Management System standard, adding other auxiliary risks such as allergens, fraud/sabotage, Kosher/Halal compliance, Rapid Alert System for Food and Feed notification, or additional specific risks such as irradiation, radioactivity, genetically modified organisms, polycyclic aromatic hydrocarbons, African swine fever, peste of small ruminants, etc. depending on the specific technological process or ingredients. Simultaneously, it identifies causes for each operation in the technological flow based on the 5M diagram: Man, Method, Material, Machine, and Environment. For each identified risk and cause, its impact was determined according to its severity and likelihood of occurrence. The final effect is defined as the risk class, calculated as the arithmetic mean of the impact derived at each process stage based on the identified risks and causes. Within the study, the methodology was applied to the spring water bottling process. This provided a new perspective on analyzing the risk factors during the bottling operations by concurrently using Ishikawa diagrams and HACCP principles throughout the product’s technological flow. The results of the study can form new methodologies aimed at enhancing sustainable food safety management strategy. In risk assessment using these two tools, the possibility of cumulative or synergistic effects is considered, resulting in better control of all factors that may affect the manufacturing process. This new perspective on studying the dynamics of risk factor analysis through the simultaneous use of the fishbone diagram and the classical HACCP system can be extrapolated and applied to any manufacturing process in the food industry and beyond.

Identification and Prioritization of Criteria Affecting the Location of Safe Assembly Points Using the Best-Worst Method

Introduction: Identifying secure gathering spots during emergencies is a crucial step in emergency management across multiple industries, particularly in oil, gas, and petrochemical fields. This study was designed to identify and rank the factors affecting the placement of safe assembly points for implementation in the country’s process industries. Materials and Methods: This descriptive-exploratory study was conducted at the National Petrochemical Company. During the initial phase, criteria affecting the selection of safe assembly points were determined by examining scientific literature, standards, guidelines, along with national and international directives. A Delphi study involving 15 experts in emergency management experts was subsequently conducted to finalize the criteria list . During the second phase, these criteria were prioritized and ranked through a multi-criteria decision-making method utilizing and the Best-Worst Method. Results: The Delphi study was completed after two rounds, revealing 10 internal and 7 external criteria, amounting to 17 criteria that affect the determination of safe assembly points during emergencies in processing industries. Within the internal criteria, accessibility was the highest rated (weight: 0.1948), vulnerability to incidents was second (weight: 0.1543), and travel time was third (weight: 0.1109). Among the external criteria, the distance from nearby hazardous centres was the highest (weight: 0.2181), the type of potential scenarios was second (weight: 0.2081), and nearby emergency facilities placed third (weight: 0.1462). Conclusion: In process industries, the placement of safe assembly points during emergencies must take into account factors like the distance from surrounding hazardous areas, the type of possible incidents, ease of access, vulnerability to incidents, nearby emergency services, and wind patterns to guarantee optimal personnel protection.

Modeling and Evaluation of Attention Mechanism Neural Network Based on Industrial Time Series Data

Chemical process control systems are complex, and modeling the controlled object is the first task in automatic control and optimal design. Most chemical process modeling experiments require test signals to be applied to the process, which may lead to production interruptions or cause safety accidents. Therefore, this paper proposes an improved transformer model based on a self-attention mechanism for modeling industrial processes. Then, an evaluation mechanism based on root mean square error (RMSE) and Kullback–Leibler divergence (KLD) metrics is designed to obtain more appropriate model parameters. The Variational Auto-Encoder (VAE) network is used to compute the associated KLD. Finally, a real nonlinear dynamic process in the petrochemical industry is modeled and evaluated using the proposed methodology to predict the time series data of the process. This study demonstrates the validity of the proposed transformer model and illustrates the versatility of using an integrated modeling, evaluation, and prediction scheme for nonlinear dynamic processes in process industries. The scheme is of great importance for the field of industrial soft measurements as well as for deep learning-based time series prediction. In addition, the issue of a suitable time domain for the prediction is discussed.

Surface-Enhanced Raman Spectroscopy for the Detection of Reactive Oxygen Species.

Reactive oxygen species (ROS) play fundamental roles in various biological and chemical processes in nature and industries, including cell signaling, disease development and aging, immune defenses, environmental remediation, pharmaceutical syntheses, metal corrosion, energy production, etc. As such, their detection is of paramount importance, but their accurate identification and quantification are technically challenging due to their transient nature with short lifetimes and low steady-state concentrations. As a highly sensitive and selective analytical technique, surface-enhanced Raman spectroscopy (SERS) is promising for detecting ROS in real-time, enabling in situ monitoring of ROS-involved electrochemical and biochemical events with exceptional resolution. This review provides a comprehensive analysis of the state-of-the-art in the SERS-based detection of ROS. Herein, the principles and ROS sensing mechanisms of SERS have been critically evaluated, highlighting their emerging applications in direct and indirect ROS monitoring in electrochemical and biological systems. The developments and reaction schemes of selective SERS probes for superoxide (•O2-), hydroxyl radicals (•OH), nitric oxide (•NO), peroxynitrite (ONOO-), and hypochlorite (OCl-) are presented. Finally, technical challenges and future research directions are discussed to guide the design of SERS for ROS analysis.

MISMS: a multistep-ahead and interpretable sequential modelling scheme for the long-term dynamic forecasting in process industries

This paper introduces Multistep-ahead and Interpretable Sequential Modeling Scheme (MISMS), a pioneering approach for long-term dynamic forecasting that enhances model interpretability in process industries. MISMS is distinguished by its innovative integration of multivariate analysis, multistep-ahead forecasting, and sequential model interpretability. The scheme incorporates knowledge embedding and Exploratory Data Analysis (EDA) to structure multiple variables and extract their temporal attributes. Based on this groundwork, sequence-to-sequence (Seq2Seq) submodels are developed to capture domain-wide dynamics rather than one single value. Crucially, MISMS has pioneered the application of sequence interpretation for model decomposition and transparency, improving interpretability. The proposed scheme employs different temporal models in practical scenarios and can ensure a better prediction with long-term accuracy and stability. Additionally, discussions on the model explainer's expected and unexpected outcomes are conducted, providing potential avenues for future research in process industries.

Reaction Engineering for Asymmetric R‐/S‐PAC Synthesis With Ephedrine or Pseudoephedrine Dehydrogenase in Pickering Emulsion

ABSTRACTThe synthesis of enantiopure α‐hydroxy ketones, particularly R‐ and S‐phenylacetylcarbinol (PAC), represents an important process in the pharmaceutical industry, serving as a pivotal step in the production of drugs. Recently, two novel enzymes, ephedrine dehydrogenase (EDH) and pseudoephedrine dehydrogenase (PseDH), have been described. These enzymes enable the specific reduction of 1‐phenyl‐1,2‐propanedione (PPD) to R‐PAC and S‐PAC, respectively. In this study, we transferred these enzymes into Pickering emulsions, which is an attractive reaction set‐up for large‐scale synthesis. The bioactive w/o Pickering emulsion (bioactive Pickering emulsion [BioPE]), in which methyl tert‐butyl ether served as the continuous phase, was stabilized by silica nanoparticles. Formate dehydrogenase from Rhodococcus jostii was utilized for cofactor regeneration. Given the considerable complexity of the BioPE, this reaction system underwent a first‐time application of design of experiment (DOE) for systematic engineering. A definitive screening design was employed to identify significant factors affecting space‐time yield (STY) and conversion. Response surface methodology was used to optimize the conditions, resulting in the observation of a high STY of 4.2 g L⁻¹ h⁻¹ and a conversion of 83.2% for BioPE with EDH, and an STY of 4.4 g L⁻¹ h⁻¹ and a conversion of 64.5% for BioPE with PseDH.

Fostering industrial symbiosis in process industries: an innovation policy perspective

Fostering industrial symbiosis in process industries: an innovation policy perspective

Novel Approach for Efficient Separation of Primary Amines Using Supercritical Fluid Chromatography.

Efficient enantioselective separation is a critical process in pharmaceutical and chemical industries for the production of chiral compounds. Herein, we developed a novel approach for the efficient enantioselective separation of primary amines using supercritical fluid chromatography (SFC) with a commercially available SFC column, Cel1. The key factors of separation, including cosolvent ratios, total cosolvent percentages, and temperature, were systematically assessed in this study. It revealed that utilization of a cosolvent mixture consisting of methanol and isopropanol in a 1:3 (v/v) ratio with a low total cosolvent percentage of 5% resulted in superior retention and selectivity. Additionally, it was observed that maintaining mid-range temperatures at 30 and 35°C achieved optimal balance between retention and efficiency. Employment of commercially available SFC columns streamlined the separation process, reducing analysis time and labor. This study highlights the effectiveness of SFC as a powerful tool for conducting high-throughput enantioselective separations in pharmaceutical and fine chemical industries.

Design and implementation of the fractional-order controllers for a real-time nonlinear process using the AGTM optimization technique

AbstractSpherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS). This system features a large dead time, and its real-time modeling generally represents a Single Input Single Output (SISO) model. However, in practice, the derived SISO model is often a First Order Plus Dead Time (FOPDT) model, necessitating an effective controller to maintain the tank’s steady-state level. In this research, the proposed AGTM method, based on the conventional Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers, is compared with the FOPI and FOPID controllers for the nonlinear SSTLLS. The performance of these controllers is contrasted using metrics such as Integral Squared Error (ISE) and Integral Absolute Error (IAE), as well as time-domain characteristics containing Rise time, Peak time, Settling time, Peak overshoot, and Steady-state error. The implementation of the aforementioned controllers is done in simulation and real-time employing the MATLAB software environment and the Data Acquisition (DAQ) device National Instrument NI-DAQmx 6211. The simulation and experimental results demonstrate the exceptional performance of the designed Fractional-Order controllers based on the proposed method which offers an increased degree of freedom despite the more complex design process.

Formation evaluation for Mauddud Formation in Bai Hassan oilfield, Northern Iraq

Formation evaluation is a critical process in the petroleum industry that involves assessing the petrophysical properties and hydrocarbon potential of subsurface rock formations. This study focuses on evaluating the Mauddad Formation in the Bai Hassan oil field by analyzing data obtained from well logs and core samples. Four wells were specifically chosen for this study (BH-102, BH-16, BH-86, and BH-93). The main objectives of this study were to identify the lithology of the Mauddud Formation and estimate key petrophysical properties such as shale volume, porosity, water saturation, and permeability. The Mauddud Formation primarily consists of limestone and dolomite, with some anhydrites present. It is classified as a clean formation due to its low shale volume of approximately 17%. The results of the study show a low water saturation of around 30% and an effective porosity reaching up to 32% (with an average of 11%). The Mauddud Formation was further analyzed using the cluster analysis method, which identified four distinct hydraulic flow units (HFUs). The permeability of the Mauddud Formation was predicted using the flow zone indicator method, revealing a range from moderate to sound quality, averaging approximately 22 md.

Development of Collaboration Model for Data Space-Based Open Collaboration Platform in Continuous Process Industries

Development of Collaboration Model for Data Space-Based Open Collaboration Platform in Continuous Process Industries

The influence of chronoanalysis on the delivery of laboratory results in a sugar-alcoholic process

Objective: The objective of this study is to describe the use of the chronoanalysis technique in laboratory processes of a medium-sized industry in the sugar and alcohol segment and its impacts on process improvement, particularly on the response time of analytical results. Theoretical Framework: Chronoanalysis is a technique based on the study of times and movements to identify and eliminate activities that do not add value to the process, aiming to optimize time and improve efficiency. Methodology/Approach: Applied research, with a descriptive objective, based on a case study of a sugar and alcohol plant, addressing qualitative aspects of the quality control sector where it presents the mapping of activities. Results: The application of chronoanalysis concepts allowed the identification and elimination of aspects that did not add value to the process, resulting in a significant reduction in analysis time by 33.65%. This improved the conditions for delivering results and facilitated the identification of flaws in the product manufacturing process. Contributions, practical and social implications: The study demonstrated that the application of chronoanalysis can have a positive impact on the efficiency of laboratory processes, reducing the turnaround time for results and contributing to improving quality and efficiency in the sugar and alcohol industry. Originality/Value: The findings could be highly relevant to other industries wanting to improve their laboratory processes and reduce the risk of non-conformities. Keywords: Chronoanalysis; Study of time and movements; Standard time; Process mapping; Analyses.

Implementation of Statistical Control Processes in Benghazi's Food Industry

Quality control is a crucial aspect of any organization aiming to reach a high-quality level by providing products or services that satisfy customer needs. Control charts are among the most effective tools used to detect the occurrence of defects in the manufacturing process so they can be fixed before being delivered to the customers. This paper aims to assess the filling machine's performance in the Al-WAHA factory. In this investigation, a control chart and process capacity were implemented. Comprehensive details on the control chart and process capacity are discussed, along with an overview of the literature on related subjects. 33 samples of 800-gram jars' worth of data were examined. The metrics for performance were computed. Through the control charts S and X, it was found that there were ten out-of-control samples in the mean chart (3, 4, 6, 7, 8, 10, 21, 23, 32, 33) and one observation in the variance chart (30) due to assignable reasons. After excluding the samples that caused the variance in the mean chart, the control charts. Both the S and X charts are within the control limits. Filling process capacity = 0.59, the process is unable to meet the specifications, and about 23.48% of the fillings are above 810 grams and 1.51% are below 790 grams. The CPK value describes the process as decentralized.

Применение технологий BigData для оптимизации производственных процессов в горнодобывающей промышленности России: анализ внедрения и эффективности

The article presents a comprehensive analysis of using the BigData technologies to optimize production processes in the Russian mining industry during the period from 2015 to 2024. The main trends and areas of the BigData application, technical and organizational solutions used, barriers and economic effects of their implementation are investigated using an extensive empirical base, including company data, industry surveys and expert assessments. An interdisciplinary approach was applied that combines the methods of statistical analysis, case studies, expert interviews and economic and mathematical modeling. The key success factors of the BigData projects in various segments of the mining industry were identified, and the drivers and barriers of digital transformation of the industry were determined. It is shown that implementation of the BigData leads to a significant increase in productivity, cost reduction and improved safety of mining operations. Recommendations on spreading the best practices were developed and promising directions of the BigData development were justified in conditions of import substitution and transition to Industry 4.0. The results of the study are of high value for the academic community, public policy in the field of digitalization and management practices of mining companies.