Industrial Process Systems Research Articles

Layer-wise-residual-driven approach for soft sensing in composite dynamic system based on slow and fast time-varying latent variables

Driven by the requirements for a comprehensive understanding of composite dynamic systems in industrial processes, this paper investigates a new soft sensor for quality prediction based on slow and fast time-varying latent variables extraction using layer-wise residuals. First, the slow feature partial least squares were expanded into long-term dependency by introducing explicit expressions of the potential state of the process into the objective function. Then, the multilayer regression model for exploring composite dynamics driven by layer-wise residuals is developed using a serial structure that can extract both slow and fast time-varying latent variables that are completely orthogonal. Finally, the exponential-weighted partial least squares are proposed for extracting fast time-varying dynamic latent variables by learning the exponential decay properties of the time-series data correlation. Case studies on the industrial debutanizer and sulfur recovery unit show that the prediction accuracy of the proposed approach outperforms traditional methods.

Fixed-bed natural gas CO2 adsorption performance of ZSM-5 zeolites impregnated with amines

This research studies the fixed-bed natural gas carbon dioxide (CO2) adsorption performance of ZSM-5 zeolites impregnation with different amines: monoethanolamine (MEA), ethylenediamine (ED), ethylenediaminetetraacetic acid (EDTA), and 4-aminophenol. The structural, morphological, and performance characteristics of the modified zeolites were examined through the use of several analytical techniques. These techniques featured Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). The thermogravimetric analysis revealed weight losses in two stages linked to the removal of bound water molecules and the breakdown of the hydroxyl structure. Through fixed bed adsorption experiments, it was discovered that ZSM-5 zeolites functionalized with MEA/ED showed the CO2 adsorption capacity. This study investigated how well four different mathematical models—Clark, Yoon-Nelson, Thomas, and Adams-Bohart—could predict CO2 adsorption breakthrough curves from natural gas using non-linear regression methods. The results showed that all models had very high adjusted determination coefficients (Adj. R²) of over 0.99, indicating they matched the experimental data really well for various adsorbents. The analysis highlighted the models' effectiveness in reflecting adsorption kinetics and capacities, particularly noting the superior performance of the Thomas, Adams-Bohart, and Yoon-Nelson models in capturing uniform breakthrough behaviors. These findings underscore the models' utility in enhancing CO2 removal processes in natural gas purification, which is pivotal for optimizing industrial gas processing systems. These results highlight the efficiency of amine-functionalized ZSM 5 zeolites in capturing CO2 from natural gas streams.

Fractional-Order Modeling and Identification for an SCR Denitrification Process

This paper presents an application of a fractional-order system on modeling an industrial process system with large inertia and time delay. The traditional integer-order model of the process system is extended to a fractional-order one in this work. To identify the parameters of the proposed fractional-order model, an output-error identification algorithm is presented. Based on the experimental step response data of the selective catalytic reduction (SCR) denitrification process in a power plant, this proposed fractional-order model shows a better fitting result compared with the typical integer-order models. An integer-order proportional–integral (PI) controller is designed for the process plant using a simple scheme according to the identified fractional-order and integer-order models, respectively. Validation tests are performed based on the obtained fractional-order and integer-order models, demonstrating the advantages of the proposed fractional-order model with the corresponding system identification approach for industrial processes with large inertia and time delay.

Intelligent neuro-computational modelling for MHD nanofluid flow through a curved stretching sheet with entropy optimization: Koo–Kleinstreuer–Li approach

Abstract The present study explores the dynamics of a two-dimensional, incompressible nanofluid flow through a stretching curved sheet within a highly porous medium. The mathematical model is formulated by including external forces such as viscous dissipation, thermal radiation, Ohmic heating, chemical reactions, and activation energy by utilizing a curvilinear coordinate system. The viscosity and thermal conductivity of the nanofluids are examined using the Koo–Kleinstreuer–Li model. The choice of $Al_{2}O_{3}$ and $CuO$ nanoparticles in this model stems from their distinct thermal properties and widespread industrial applicability. By non-dimensionalizing the governing partial differential equations, the physical model is simplified into ordinary differential equations. BVP-5C solver in MATLAB is utilized to numerically solve the obtained coupled non-linear ordinary differential equation. Graphical results are presented to investigate the velocity, temperature, and concentration profiles with entropy generation optimization under the influence of several flow parameters. The artificial neural network backpropagated with Levenberg–Marquardt method (ANN-BLMM) used to study the model. The performance is validated using regression analysis, mean square error and error histogram plots. The outcome illustrates that the velocity and temperature profiles increase with increasing the Forchhiemer parameter. Also, the velocity profile increases with increasing curvature parameter, while, reverse effect is observed for temperature profile. This research augments our comprehension of nanofluid dynamics over curved surfaces, which has implications for engineering applications. The insights gained have the potential to significantly contribute to the advancement of energy-efficient and environmentally sustainable cooling systems in industrial processes.

Dynamic data reconciliation based on elman neural network and particle filter

In the process of modern industries, complex nonlinear dynamic systems present high requirements for measured data. In the actual industrial process system, the measurement data obtained by sensors will inevitably be subject to noise disturbances from the equipment itself or from the outside environment. These noise disturbances will deteriorate the dynamic performance of the system to a certain extent and affect the industrial production. Particle filter (PF) can be used to infer the accurate outputs of nonlinear dynamic system from the contaminated measurement data, but PF is limited to the pre-known state space model. In the actual industrial process, it is difficult to summarize the internal behavior of the system and obtain the pre-known state space model. Therefore, it is impossible to directly use PF in the nonlinear dynamic system with unknown model. In order to solve the above problems, this paper proposes a dynamic data reconciliation method called ENN-PF, which combines Elman neural network (ENN) data-driven modeling with PF. In this method, ENN is used for data-driven modeling, that is, the system model is dynamically identified by using the input and output data of the system, and then the dynamic data reconciliation is carried out by using PF according to the model identified by ENN. Finally, the proposed ENN-PF method is validated by simulations and practical experiments to effectively reduce the interference of measurement noise and improve the dynamic performance of the system.

Risk causation analysis and prevention strategy of working fluid systems based on accident data and complex network theory

Working fluid systems (WFS) in industrial processes are often accompanied by hazardous gases, which lead to various hazardous accidents. This paper, based on accident data and complex network theory, develops a framework for risk causation analysis and prevention strategies for WFS. The accident components at WFS are identified with emphasis. In the case study, a set of risk event nodes, including system, equipment, personnel, environment, and accident types, are selected based on the characteristics of systems. A complex network model according to accident chains is constructed to represent the complex interactions and relationships between various factors. This model evaluates the structural characteristics of the network and elucidates the relationship between accident causes and system risks. The comprehensive importance of different nodes is analyzed in order to rank the risk factors and determine three types of critical causative paths for the risks of the accident chain. This method has been verified to help identify critical factors in accidents when combined with robustness analysis. The risk prevention strategies are proposed to reduce the risk of accident evolution. The method can help identify the accident risk characteristics of WFS, providing valuable information for safe application and accident prevention.

It takes three to tangle

The clustering of debris floating on liquid interfaces such as water surfaces is a complex phenomenon that finds its applications in numerous examples from industrial processing and environmental systems. The recent paper by Shin & Coletti (J. Fluid Mech., vol. 984, 2024, R7) presents an experimental campaign investigating the three effects of turbulence, particle interactions and interfacial effects, to elucidate how the three force scales drag, capillary forces and lubrication give rise to three distinct regimes of clustering in dense suspensions. The study, hence, provides a useful systematic to categorize the clustering mechanisms. As an important finding, it is shown that, depending on volume fraction and non-dimensional turbulent shear, particles either tend to cluster into aggregate sizes larger than the Kolmogorov scale or can break into pieces that are as small as the primary particle size.

Numerical simulation for MHD slip flow with heat transfer over a stretching bullet‐shaped object

AbstractThis paper investigates magnetohydrodynamic (MHD) boundary layer slip flow with heat transfer over a bullet shaped object. The study addresses a significant problem in fluid dynamics and heat transport with applications in various engineering and industrial domains, including aerospace, material processing, and energy systems. The governing equations are resolved using the bvp4c, an inbuilt MATLAB tool, and the arithmetic computation for the momentum and thermotic equations are executed. The results are exhibited graphically. Numerical outcomes are graphically depicted with the aid of velocity and temperature profiles for several model variables. The achieved results exhibit a promising agreement with the previously established findings available in the open literature. The heat transfer processes and fluid flow are remarkably influenced by means of the ratio of surface thickness and stretching potential. The results obtained designated that the Mixed convection parameter λ increases momentum BL thickness, whereas the temperature profile diminishes. Furthermore, momentum and temperature profile improve for surface thickness parameter . The current investigation highlights the potential utility of heat transport rate and friction factor in the industrial divisions for regulating cooling rates and enhancing the quality of end products.

Thermal analysis of AIN-Al2O3 Casson hybrid nano fluid flow through porous media with inclusion of slip impact

A mathematical analysis of magnetized Casson hybrid nanofluid flow over a stretching permeable sheet with porosity effects and being exposed to thermal convective boundary conditions with slip in velocity and concentration have been presented numerically. The attained steady-state partial differential equations are highly coupled and nonlinear in nature and are not agreeable to any of the direct techniques. Hence, they are being reduced to coupled-nonlinear ordinary differential equations by means of appropriate similarity transformations. A MATLAB-based, bvp4c scheme has been employed to obtain numerical solutions. Suitable graphs have been plotted which demonstrates the fluid flow velocity, temperature, concentration and micro-organism distribution fields in the boundary layer regime. Magnifying Casson fluid parameter and porosity leads to a decline in the velocity field. Amplifying the concentration slip decays the concentration profile. A comparative analysis was conducted to confirm the findings from previous research, demonstrating strong consistency with the results previously documented. Enhancing heat transfer in microelectromechanical systems (MEMS), optimizing medical drug delivery techniques, and boosting the efficiency of cooling systems in industrial processes are all potential applications of this study.

Exergetic and exergoeconomic analyses of a large-scale industrial beer processing system

This research evaluated the performance of components and sections involved in industrial beer production using exergetic and exergoeconomics methodologies. The system was segmented into five production sections, and three energy input types were considered. The exergetic studies showed an operational exergetic efficiency of 3.33%, with an overall exergetic destruction rate of 5.54 MW and a specific destruction rate of 9.72 kW/hl for beer production. The overall improvement potential and sustainability index were estimated at 4.98 MW and 1.03, respectively. The brewhouse and packaging hall were identified as the sections with the highest production inefficiency, 58.73% and 30.40%, respectively. The exergoeconomic studies revealed a cost rate of 0.1704 USD/s for beer production, with the wort kettle, filling and cocking machine, Kieselguhr candle filter, whirlpool, and brite beer tank identified as the top five significant components in descending order. The efficiency of the system was critically affected by the activities in the packaging hall, particularly those involving energy inputs that cannot be recovered or attributed to the processed stream, beer. Further research is required to determine the cost savings of optimization measures identified from additional steam throttling, downsizing of some main pumps, and exergy loss during heating of wort and beer chilling processes.

A Nonlinear Subspace Predictive Control Approach Based on Locally Weighted Projection Regression

Subspace predictive control (SPC) is a widely recognized data-driven methodology known for its reliability and convenience. However, effectively applying SPC to complex industrial process systems remains a challenging endeavor. To address this, this paper introduces a nonlinear subspace predictive control approach based on locally weighted projection regression (NSPC-LWPR). By projecting the input space into localized regions, constructing precise local models, and aggregating them through weighted summation, this approach handles the nonlinearity effectively. Additionally, it dynamically adjusts the control strategy based on online process data and model parameters, while eliminating the need for offline process data storage, greatly enhancing the adaptability and efficiency of the approach. The parameter determination criteria and theoretical analysis encompassing feasibility and stability assessments provide a robust foundation for the proposed approach. To illustrate its efficacy and feasibility, the proposed approach is applied to a continuous stirred tank heater (CSTH) benchmark system. Comparative results highlight its superiority over SPC and adaptive subspace predictive control (ASPC) methods, evident in enhanced tracking precision and predictive accuracy. Overall, the proposed NSPC-LWPR approach presents a promising solution for nonlinear control challenges in industrial process systems.

Dufour and Soret diffusions phenomena for the chemically reactive MHD viscous fluid flow across a stretching sheet with variable properties

The flow of fluid on a slandering stretchable sheet with variable thickness can have several engineering applications like design and optimization of, aerodynamic structures such as wings and airfoils, wind turbine blade design, efficient cooling systems for electronic devices, power plants and industrial processes. Keeping in view these important applications, this work explores Darcy-Forchheimer flow of magnetohydrodynamics (MHD) fluid across a slandering stretchable sheet with variable thickness in the presence of chemically reactive effects, and heat source. The flow is impacted by the combined influences of diffusive-thermo and thermo-diffusive phenomena. The fluid is set into motion along x-axis due to the stretching behavior of the sheet with y-axis as normal to it. Similarity transformations are employed to convert the modeled equations into nonlinear ordinary differential equations. It has observed as outcomes of this study that velocity distribution has augmented with expansion in viscosity factor and has declined with growth in permeability factor, inertia factor, and magnetic factor for both scenarios h1=0.0 and h1=0.5 where the impacts are more significant for the scenario h1=0.0. Thermal distribution has declined with augmentation in Prandtl number, permeability factor and Dufour number while has upsurge with escalation in thermal source factor for both scenarios h2=0.0 and h2=0.5. Concentration distribution has diminished with escalation in Schmidt number, chemical reactivity factor and has augmented with growth in Soret factor for both the scenarios h3=0.0 and h3=0.5 with more significant impacts in the scenario h3=0.0. Stream lines and contour lines for fluid flow have also sketched for the scenarios h1=0.0 and h1=0.5 as well as for M=0.0 and M=2.0. Validation of the proposed model and the methodology has ensured through comparison with fine agreement amongst current results and previously published work.

Wet-Chemically Grown Interfacial Oxide for Passivating Contacts Fabricated With an Industrial Inline Processing System

Wet-Chemically Grown Interfacial Oxide for Passivating Contacts Fabricated With an Industrial Inline Processing System

A network structure for industrial process fault diagnosis based on hyper feature extraction and stacked LSTM

Traditional feature extraction methods are highly dependent on manual work, and manual extraction will inevitably ignore some potentially useful features. For complex systems, there is no simple one-to-one correspondence between faults and symptoms, and fault characteristics often have complex characteristics such as hierarchy, propagation correlation, and delay. In the existing research results, the convolutional neural network (CNN) has been proven to be an effective network structure. But the existing network structure is more like a classifier, and only some features of industrial data are considered in the feature extraction stage, which will cause some feature information to be lost during the training process. To effectively solve the above problems, this paper proposes an industrial process-oriented hyper feature extraction network structure. This method first extracts multi-level CNN fault features, aggregates the hierarchical feature maps and compresses them in a unified feature space to form hyper features. Then the hyper features formed by feature fusion are input to the stacked Long Short Term Memory (LSTM) network for further feature extraction. In this way, the information in the time series can be effectively transmitted, and at the same time, the problem of gradient disappearance caused by long-term training can be solved. We conducted experiments on the Tennessee-Eastman (TE) benchmark process and industrial coking oven datasets. The experimental results show that the proposed method can effectively improve the fault diagnosis accuracy of the industrial production process system.

Thermal performance analysis of heat pipe heat exchanger for effective waste heat recovery

The importance of waste heat recovery systems for various industrial processes has been growing steadily, driven by their potential to improve energy efficiency, reduce fuel consumption, and minimize environmental impacts. Heat exchangers are key components in these systems, facilitating the transfer of heat from waste sources to useful media. Among them, heat pipe heat exchangers (HPHX) have been widely employed in industries such as steel and ceramic as an energy-saving measure to cut carbon emissions. Previous studies have mainly focused on experimental analyses of HPHX thermal performance. However, due to the high cost of full-scale thermal tests, there is a lack of research investigating the effect of detailed geometrical and operational variations on HPHX thermal performance. This study addresses the existing gap by conducting a comprehensive conjugate numerical investigation to explore the influence of flow path structure and condenser area on the performance of an air-to-liquid HPHX. We first utilize a full-scale conjugate simulation to propose an optimized HPHX design that enhances both heat transfer rate and effectiveness through the variation of baffle configurations and condenser height. Subsequently, we experimentally validate the proposed design through a full-scale HPHX thermal test. Results revealed that the baffle design significantly improved the heat transfer rate and effectiveness by up to 36.3% and 36.0%, respectively. Conversely, increasing the condenser height by five times enhanced the heat transfer rate by only 3.5%, with the condenser area showing a minor impact owing to the higher heat capacity rate of water compared to air. The outcomes revealed remarkable energy-recovery capabilities, revealing a total thermal energy wastage recovery rate up to 19.7 kW. These findings demonstrate the potential and effectiveness of the proposed HPHX in waste heat recovery applications.

A circular economy for reusable plastic packaging: digital assessment for cleaning assurance

Single use plastic packaging is widely accepted to cause significant negative environmental impacts. One option to reduce these impacts is to develop commercially viable and socially acceptable reuse systems. Such closed loop packaging systems require careful consideration of reverse (or circular) logistics, financial modelling, and quality control (fulfilling legal requirements and ethical obligations). One of the most prominent barriers to the concept of reusable packaging for food is safety, dictated by challenges of effective cleaning and pack integrity assessment. This research directly addresses cleaning assurance by investigating the application of ultraviolet (UV) induced fluorescence imaging to optically detect residual food fouling on plastic packaging surfaces. This research reports for the first time the evaluation of UV fluorescence imaging @370 nm for the detection of food fouling on washed PET food pots. The technique is compared against the industry standard, adenosine triphosphate swabbing, and is found to provide a comparable range of sensitivity, full surface coverage, faster assessment, and is suitable for full automation without the use of consumables. The technique is evaluated with respect to integration into industrial process systems. Plastic packing design implications are discussed in this context.

Industrial Metaverse-Based Intelligent PID Optimal Tuning System for Complex Industrial Processes.

In this article, the method of dynamic performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes is proposed. This method combines the digital twin model of the PID control process based on system identification and adaptive deep learning and the PID tuning intelligent algorithm based on reinforcement learning with virtual reality and immersive interaction of industrial metaverse. An industrial metaverse-based intelligent PID tuning system is proposed by combining the above method with the end-edge-cloud collaboration technology of Industrial Internet. The challenging problem that the actual operating PID control system in complex industrial processes cannot be optimized online is solved. Using the energy-intensive equipment, the fused magnesium furnace, as an industrial object, we conducted comparative simulation experiments between the proposed control method and several advanced control methods, as well as industrial experiments for the proposed intelligent system. Simulation experiments demonstrate the effectiveness of the proposed control method. The industrial experimental results indicate that the performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes can be realized, which achieves excellent control effects.

Entropy generation in a ciliary flow of an Eyring-Powell ternary hybrid nanofluid through a channel with electroosmosis and mixed convection.

Drug delivery systems, where the nanofluid flow with electroosmosis and mixed convection can help in efficient and targeted drug delivery to specific cells or organs, could benefit from understanding the behavior of nanofluids in biological systems. In current work, authors have studied the theoretical model of two-dimensional ciliary flow of blood-based (Eyring-Powell) nanofluid model with the insertion of ternary hybrid nanoparticles along with the effects of electroosmosis, magnetohydrodynamics, thermal radiations, and mixed convection. Moreover, the features of entropy generation are also taken into consideration. The system is modeled in a wave frame with the approximations of large wave number and neglecting turbulence effects. The problem is solved numerically by using the shooting method with the assistance of computational software "Mathematica" for solving the governing equation. According to the temperature curves, the temperature will increase as the Hartman number, fluid factor, ohmic heating, and cilia length increase. It is also disclosed that ternary hybrid nanoparticles result in a change in flow rate when other problem parameters are varied, and the same is true for temperature graphs. Engineers and scientists can make better use of nanofluid-based cooling systems in electronics, automobiles, and industrial processes with the aid of the study's findings.

Assessment of Safety Barrier Performance in Environmentally Critical Facilities: Bridging Conventional Risk Assessment Techniques with Data-Driven Modelling

The failure of emission control systems in industrial processes undergoing emission regulations can cause severe harm to the environment. In this context, safety engineering principles can be applied to analyze process deviations and identify suitable safety barriers to mitigate harmful emissions during critical events. However, the selection, design, and assessment of proper safety barriers may be complex due to several contingencies such as the inability to perform extensive field tests on systems under strict emission regulations. In this study, an approach is proposed to couple conventional hazard identification techniques with a digital model of a flue gas treatment system to support the identification and performance assessment of safety barriers for emission control. Resilience analysis is used to evaluate the behavior of the most relevant safety barrier options, selected through a screening with conventional hazard identification tools. Barriers are simulated using the digital model of the system, gathering key information for their design and evaluation, and overcoming the limitations to field tests at the real plant. The methodology is illustrated with reference to acid gas removal in waste-to-energy facilities, a relevant example of an emission control system that is typically exposed to significant process deviations.

Material Challenges and Hydrogen Embrittlement Assessment for Hydrogen Utilisation in Industrial Scale

Hydrogen has been studied extensively as a potential enabler of the energy transition from fossil fuels to renewable sources. It promises a feasible decarbonisation route because it can act as an energy carrier, a heat source, or a chemical reactant in industrial processes. Hydrogen can be produced via renewable energy sources, such as solar, hydro, or geothermic routes, and is a more stable energy carrier than intermittent renewable sources. If hydrogen can be stored efficiently, it could play a crucial role in decarbonising industries. For hydrogen to be successfully implemented in industrial systems, its impact on infrastructure needs to be understood, quantified, and controlled. If hydrogen technology is to be economically feasible, we need to investigate and understand the retrofitting of current industrial infrastructure. Currently, there is a lack of comprehensive knowledge regarding alloys and components performance in long-term hydrogen-containing environments at industrial conditions associated with high-temperature hydrogen processing/production. This review summarises insights into the gaps in hydrogen embrittlement (HE) research that apply to high-temperature, high-pressure systems in industrial processes and applications. It illustrates why it is still important to develop characterisation techniques and methods for hydrogen interaction with metals and surfaces under these conditions. The review also describes the implications of using hydrogen in large-scale industrial processes.