Real-time Optimization Research Articles

Improved Assistive Profile Tracking of Exosuit by Considering Adaptive Stiffness Model and Body Movement.

Wearable robots have been developed to assist the physical performance of humans. Specifically, exosuits have attracted attention due to their lightweight and soft nature, which facilitate user movement. Although several types of force controllers have been used in exosuits, it is challenging to control the assistive force due to the material's softness. In this study, we propose three methods to improve the performance of the basic controller using an admittance-based force controller. In method A, the cable was controlled according to the user's thigh motion to eliminate delays in generating the assistive force and improve the control accuracy. In method B, the stiffness feedforward model of the human exosuit was divided into two independent models based on the assistance phase for compensating the nonlinear stiffness more accurately. In method C, the real-time optimization method for the stiffness feedforward model with an adaptive moment estimation method optimizer was proposed. To validate these methods' effectiveness, we designed three new controllers, gradually combined the proposed methods with the basic controller, and compared their performances. We found that controller III, combining all three methods with the basic controller, showed the best performance. By applying controller III in the same exosuit, the root-mean-square error of the assistive force decreased from 39.84 N to 13.72 N, reducing the error by 65.56% compared with the basic controller. Moreover, the time delay for force generation in the gait cycle percentage decreased from 9.99% to 3.41%, reducing the delay by 65.87% compared with the basic controller.

Addressing multi-step inverse heat transfer problems via reduced order models in a cooling process for polymer pipes with sparse measurements

In modern polymer pipe extrusion processes with connected post-processing steps, the intermediate cooling and conditioning process is essential for the resulting product quality. In such cases, computationally efficient models are required for real-time process control and optimization. Unfortunately, heat transfer coefficients are rarely known for actual production processes, leading to an inverse heat transfer problem. Existing methods mainly rely on computationally intensive numerical models or on data intensive neural networks. Both are not suitable, if only sparse measurements are available and a fast execution time is required. In contrast to that, we propose to use a proper orthogonal decomposition and project the governing heat equation onto extracted basis functions via the Galerkin method. This leads to an efficient and accurate reduced order model, which can be used for parameter identification and subsequent process control. Regarding the identification, we address the ill-posed problem with well-known relative constraints, l2 regularization and decomposition of coefficients based on the Nusselt number. Simulation and experimental results show that the heat transfer coefficients are consistently identified and robust against initial parameter guesses despite sparse and noisy temperature measurements. Consequently, the proposed method can be efficiently applied in cooling and conditioning processes in polymer extrusion and related applications such as identifying the heat transfer coefficients and thermal resistance in pressurized surge lines of nuclear power plants and brick walls in melting furnaces respectively.

Development and Application of Digital Twin Control in Flexible Manufacturing Systems

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.

In-melt electron analysis to accelerate process exploration of ceramics: Electron beam melting of TiB2

To enhance the versatility of electron beam powder bed fusion (EB-PBF), a widely utilized additive manufacturing (AM) technique for metallic materials, we propose a novel paradigm aimed at facilitating the exploration of the process parameters for less-studied materials, such as ceramics. The high melting points and poorly understood thermal properties of ceramics have constrained the comprehension of their melting behavior. In this study, titanium diboride (TiB2) sintered bodies were subjected to spot melting under four distinct electron beam currents and four different exposure times. By introducing a novel in-melt electron analysis (IMEA) approach, the various stages of melting were clearly identified. The analysis and interpretation of IMEA signals were found to be consistent with experimental observations on the spot-melted surface of TiB2. IMEA demonstrates significant potential for real-time process window optimization and quality assurance for challenging and novel materials.

STRATEGIC APPROACHES TO LEAN MANUFACTURING IN INDUSTRY 4.0: A COMPREHENSIVE REVIEW STUDY

This systematic review, based on the analysis of 130 peer-reviewed articles, explores the strategic integration of lean manufacturing with Industry 4.0 technologies, focusing on the role of the Internet of Things (IoT), artificial intelligence (AI), and big data analytics in enhancing lean practices. Using the PRISMA guidelines, the study identified key synergies between lean and Industry 4.0, demonstrating how these technologies facilitate real-time data collection, predictive maintenance, and process optimization, all of which align with lean’s core principles of waste reduction and continuous improvement. The review also highlights significant challenges to this integration, including high implementation costs, workforce skill gaps, and resistance to organizational change, particularly within small and medium-sized enterprises. Additionally, emerging trends suggest that the future of lean-Industry 4.0 integration will increasingly focus on sustainability, with companies leveraging digital tools to enhance energy efficiency and reduce environmental waste. The findings underscore the need for further research to address these challenges and explore scalable solutions that can drive the broader adoption of lean-Industry 4.0 integration in diverse industrial contexts.

Active-passive hybrid feed rate control systems in CNC machining: Mitigating force fluctuations and enhancing tool life

Real-time optimization of machining processes for aerospace structural components is imperative due to the difficult-to-cut materials and complex structures. Effective feed rate control in CNC machining plays a key role in achieving high-quality results. While current research trends in mass production emphasize the utilization of adaptive control algorithms and controllers within machining systems, there remains a need to enhance the adaptability of these control systems. This study introduces an active-passive hybrid feed rate control system designed to maintain consistently stable cutting conditions and extend tool life. The hybrid feed rate control system combines offline active pre-compensating, a scheduled pre-compensating feed rate profile, and an online feed rate passive fine-tuning with a real-time adaptive control loop in computer numerical control (CNC) machining. The response speed is enhanced by offline active pre-compensation, whereas the control precision is improved by online passive fine-tuning with a fuzzy controller. Four control cases were tested separately throughout the tool lifespan, including the conventional and adaptive control methods. The proposed adaptive control method reduced the maximum slope from 3.6 to 1.2, demonstrating superior performance compared to both its individual components and other case studies. The results showed a significant 25 % increase in tool life, with a slight decrease in machining efficiency of 7.35 % during the entire tool lifespan.

Real-Time Optimization of Ancillary Service Allocation in Renewable Energy Microgrids Using Virtual Load

The stability of global economies relies heavily on power systems (PS) that have sufficient operating reserves. When these reserves are insufficient, power systems become vulnerable to issues such as load shedding or complete blackouts. Maintaining grid stability becomes even more challenging with a high penetration of renewable energy sources (RES). However, RES, connected through power electronic devices, offer significant potential as ancillary service (AS) sources. Renewable energy-based microgrids (MG), which aggregate various RES resources and have substantial load control potential, further enhance the capability of AS provision from RES. The presence of diverse AS resources raises the question of how to dispatch ancillary service signals optimally to all resources. Most of the previous research work related to AS allocation relied on single-bus MG models. This paper proposes a detailed MG model for the optimal dispatching of ASs among the resources using Virtual Load, along with an optimization procedure to achieve the best results. The model incorporates voltage profiles and power losses for AS dispatching, and a comparative analysis is conducted to quantify the significance of grid modeling. The model and proposed procedure are tested using the CIGRE microgrid benchmark model. The results indicate that detailed modeling of MG can impact the results by 11%, compared to single-bus modeling, which qualifies detailed MG modeling for all future research work and shows the impact that modeling can have on technical and economic indicators of MG operation.

Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology

In response to the problems of insufficient model accuracy and poor real-time performance in real-time monitoring and optimization of data models in traditional power systems, this paper explored them based on machine learning. It used methods such as box plots and Kalman filters to preprocess power data and used Fourier transform and long short-term memory (LSTM) network to extract data features. Based on long-term and short-term memory networks, this paper constructed a deep neural network model to process data. Recursive feature elimination method can be used for feature selection in the model, and multi-model integration method can be used for feature fusion. The experiment trained and tested the model on two datasets, IEEE 39-Bus and Pecan Street. The experimental results show that the accuracy of the paper's model in both the training and testing sets is higher than that of the convolutional neural network, decision tree, and support vector machine models in the comparative experiments, reaching 93 % and 94 %, respectively. The average response times in three different testing methods were 139.8 ms, 151 ms, and 140.6 ms, respectively. The lowest daily failure rate for 30 consecutive days of work was 0 %, which reflects the stability of the model; in the satisfaction survey of practitioners, the proportion of high recognition answers to each question was higher than 80 %. The experimental results show that the machine learning intelligent control system has achieved good application in power data modeling technology, providing a reference for similar research in the future.

Performance Analysis and Rapid Optimization of Vehicle ORC Systems Based on Numerical Simulation and Machine Learning

The organic Rankine cycle (ORC) system is an important technology for recovering energy from the waste heat of internal combustion engines, which is of significant importance for the improvement of fuel utilization. This study analyses the performance of vehicle ORC systems and proposes a rapid optimization method for enhancing vehicle ORC performance. This study constructed a numerical simulation model of an internal combustion engine-ORC waste heat recovery system based on GT-Suite software v2016. The impact of key operating parameters on the performance of two organic Rankine cycles: the simple organic Rankine cycle (SORC) and the recuperative organic Rankine cycle (RORC) was investigated. In order to facilitate real-time prediction and optimization of system performance, a data-driven rapid prediction model of the performance of the waste heat recovery system was constructed based on an artificial neural network. Meanwhile, the NSGA-II multi-objective algorithm was used to investigate the competitive relationship between different performance objective functions. Furthermore, the optimal operating parameters of the system were determined by utilizing the TOPSIS method. The results demonstrate that the highest thermal efficiencies of the SORC and RORC are 6.21% and 8.61%, respectively, the highest power outputs per unit heat transfer area (POPAs) are 6.98 kW/m2 and 8.99 kW/m2, respectively, the lowest unit electricity production costs (EPC) are 7.22 × 10−2 USD/kWh and 3.15 × 10−2 USD/kWh, respectively, and the lowest CO2 emissions are 2.85 ton CO2,eq and 3.11 ton CO2,eq, respectively. The optimization results show that the RORC exhibits superior thermodynamic and economic performance in comparison to the SORC, yet inferior environmental performance.

Developing an automatic integration approach to generate brick model from imperfect building information modelling

Recent advanced architectural algorithms, such as real-time optimization control and fault diagnostics, have garnered significant interest for their ability to reduce building energy consumption and improve equipment maintenance efficiency. However, deploying these algorithms in actual buildings often involves significant manual effort and time to align building data points with algorithmic requirements. In response, standardized ontologies like the Brick Schema have been introduced. This ontology streamlines the representation of building topology, equipment, and data points, as well as algorithm inputs, enabling quicker algorithm integration and deployment. Despite these advancements, manually constructing Brick models from scratch remains time-consuming, laborious, and prone to errors, presenting a new challenge in rapid model development. To overcome these issues, some researchers have turned to universal BIM formats, such as IFC/Revit, for accelerated Brick modeling. Nevertheless, the prevalence of errors in human-generated BIM models often impedes effective conversion. To efficiently convert imperfect IFC models into Brick models, this paper presents a novel BIM to Brick methodology comprising three key steps: IFC inspection and repair, IFC to Brick semantic modeling, and dataset mapping. This method ensures high-quality reuse of even imperfect IFC models. Our case study demonstrates that this method can produce an ideal Brick model from a flawed BIM model, meeting the semantic metadata requirements specified by the BuildingMOTIF tool. This innovative approach substantially reduces the effort required to generate Brick models, effectively handling the inaccuracies and noise inherent in real-world BIM.

Efficiency-Oriented Model Predictive Control: A Novel MPC Strategy to Optimize the Global Process Performance.

Existing control strategies, such as Real-time Optimization (RTO), Dynamic Real-time Optimization (DRTO), and Economic Model Predictive Control (EMPC) cannot enable optimal operation and control behavior in an optimal fashion. This work proposes a novel control strategy, named the efficiency-oriented model predictive control (MPC), which can fully realize the potential of the optimization margin to improve the global process performance of the whole system. The ideas of optimization margin and optimization efficiency are first proposed to measure the superiority of the control strategy. Our new efficiency-oriented MPC innovatively uses a nested optimization structure to optimize the optimization margin directly online. To realize the computation, a Periodic Approximation technique is proposed, and an Efficiency-Oriented MPC Type I is constructed based on the Periodic Approximation. In order to alleviate the strict constraint of Efficiency-Oriented MPC Type I, the zone-control-based optimization concept is used to construct an Efficiency-Oriented MPC Type II. These two well-designed efficiency-oriented controllers were compared with other control strategies over a Continuous Stirred Tank Reactor (CSTR) application. The simulation results show that the proposed control strategy can generate superior closed-loop process performance, for example, and the Efficiency-Oriented MPC Type I can obtain 7.11% higher profits than those of other control strategies; the effectiveness of the efficiency-oriented MPC was, thereby, demonstrated.

Evolutionary method of digital twin model for building physics mechanism

ABSTRACT Building digital twin (BDT) realizes real-time monitoring, prediction, and optimization control of the building system through the synchronization between the real world and the virtual platform, and improves the efficiency of building operation and maintenance. However, the changes in physical laws caused by the decline and aging of buildings make it difficult for the BDT models to depict the dynamic evolution mechanism of buildings and improve decision-making, which brings great challenges to the prediction and maintenance of buildings. To this end, an evolutionary method of digital twin model for building physics mechanisms is proposed, which integrates the internet of things (IoT), building information physical model (BIPM), and machine learning algorithms to build a self-evolution framework of digital twins for building physics mechanism, and supports high-precision modeling of BDTs. The advanced prediction (AP) model and autonomous decision-making (ADM) model for building physics mechanisms with self-evolution capability are designed, which support the autonomous correction of model parameters and the dynamic updating of the model driven by real-time data, and the ability to learn optimal control actions through interactive trial and error with the environment. The experimental results based on the settlement prediction and control case of underground engineering show that this evolutionary method can dynamically simulate the future evolution trend of the settlement law of underground engineering, and adjust the physical parameters to the ideal range to guide the evolution of underground engineering in a better direction. Compared with the existing methods, it has better prediction accuracy, interpretability, and generalization performance.

Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers

The integration of renewable energy sources into an energy grid introduces volatility, challenging grid stability and reliability. To address these challenges, this work proposes a two-stage optimization approach for the operation of electrolyzers used in green hydrogen production. This method combines site-wide and real-time optimization to manage a fluctuating energy supply effectively. By leveraging the dual use of an existing optimization model, it is applied for both site-wide and real-time optimization, enhancing the consistency and efficiency of the control strategy. Site-wide optimization generates long-term operational plans based on long-term forecasts, while real-time optimization adjusts these plans in response to immediate fluctuations in energy availability. This approach is validated through a case study showing that real-time optimization can accommodate renewable energy forecast deviations of up to 15%, resulting in hydrogen production 6.5% higher than initially planned during periods of increased energy availability. This framework not only optimizes electrolyzer operations but can also be applied to other flexible energy resources, supporting sustainable and economically viable energy management.

Steady-state and dynamic simulation of gas phase polyethylene process

Gas-phase polyethylene (PE) processes are among the most important methods for PE production. A deeper understanding of the process characteristics and dynamic behavior, such as properties of PE and reactor stability, holds substantial interest for both academic researchers and industries. In this study, both steady-state and dynamic models for a gas-phase polyethylene process are established as a simulation platform, which can be used to study a variety of operation tasks for commercial solution polyethylene processes, such as new product development, process control and real-time optimization. The copolymerization kinetic parameters are fitted by industrial data. Additionally, a multi-reactor series model is developed to characterize the temperature distribution within the fluidized bed reactor. The accuracy in predicting melt index and density of the polymer, and the dynamic behavior of the developed models are verified by real plant data. Moreover, the dynamic simulation platform is applied to compare four practical control schemes for reactor temperature by a series of simulation experiments, since temperature control is important in industrial production. The results reveal that all four schemes effectively track the setpoint temperature. However, only the demineralized water temperature cascade control demonstrates excellent performance in handling disturbances from both the recycle gas subsystem and the heat exchange subsystem.

Globalization of distributed parameter self‐optimizing control

AbstractNumerous nonlinear distributed parameter systems (DPSs) operate within an extensive range due to process uncertainties. Their spatial distribution characteristic, combined with nonlinearity and uncertainty, poses challenges in optimal operation under two‐step real‐time optimization (RTO) and economic model predictive control (EMPC). Both methods necessitate substantial computational power for prompt online reoptimization. Recent local distributed parameter self‐optimizing control (DPSOC) achieves optimality without repetitive optimization. However, its effectiveness is confined to a narrow range around a nominal operation. Here, globalized DPSOC is introduced to surmount the limitation of the local DPSOC. A global loss functional concerning controlled variables (CVs) is formulated using linear operators and Fubini's theorem. Minimizing the loss with a numerical optimization procedure yields CVs exhibiting global optimality. Maintaining these CVs at constants ensures such a process has a minimal average loss in a large operating space. The effectiveness of the proposed method is substantiated through a transport reaction simulation.

Treatment of pulp and paper mill effluent through combined aerobic and anaerobic suspended fixed-bed bioreactor.

This study explored using ultrafiltration (UF) membranes to treat pulp and paper mill wastewater, implementing a novel Taguchi experimental design to optimize operating conditions for pollutant removal and minimal membrane fouling. Researchers examined four factors: pH, temperature, transmembrane pressure, and volume reduction factor (VRF), each at three levels. Optimal conditions (pH10, 25°C, 6bar, VRF 3) led to a 35% reduction in flux due to fouling and high pollutant rejections: total hardness (83%), sulfate (97%), spectral absorption coefficient (SAC254) (95%), and chemical oxygen demand (COD) (89%). Conductivity had a lower rejection rate of 50%. Advanced imaging techniques like atomic force microscopy (AFM) and scanning electron microscopy (SEM) revealed reduced membrane fouling under these conditions. The Taguchi method effectively identified optimal conditions, significantly improving wastewater treatment efficiency and promoting environmental sustainability in the pulp and paper industry. PRACTITIONER POINTS: This study optimized UF membrane conditions for pulp and paper mill wastewater, reducing fouling and enhancing pollutant removal, offering practical strategies for industrial treatment. AFM and SEM provided key insights into membrane fouling and mitigation, promoting real-time diagnosis and optimization for enhanced treatment efficiency. Prioritizing anaerobic fixed-bed systems in wastewater treatment is beneficial for achieving high COD removal efficiency. Optimizing hydraulic retention time (HRT) in these systems can further improve their overall effectiveness and sustainability.

Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning

Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning

Synchronisation of thermal imaging and multi-channel EIS to interpret planar array PCB fuel cell performance

The planar design of printed circuit board (PCB) fuel cells offers the advantages of simple structure, rapid prototyping and easy manufacturing. However, uneven reactants and heat distribution in each PCB cell module often result in inconsistent overall performance. Current characterisation methods mainly focus on the study of the overall performance and lack the analysis of individual cells, which provide a limited understanding of localised electrochemical and thermal effects that might play a critical part in further optimising and diagnosing the cell performance of this category. We have developed a multi-channel impedance diagnostic technique for PCB fuel cells. Combining with thermal imaging, we could simultaneously diagnose individual open cathode cell modules by measuring the heat distribution and electrochemical impedance spectra (EIS) of 11 PCB cell modules. Hence, the correlation between heat distribution, internal resistance and performance of different sections of PCB fuel cells can be established. By comparing the fuel cell’s pristine and degraded, we demonstrated how this non-destructive technique could identify and locate cell modules with deteriorated performance, which would be beneficial to real-time diagnostics, quality control and optimisation. The multi-channel impedance measurement takes several seconds only through optimised multi-frequency perturbation and can be readily extended to stacks containing more cells by simply increasing the number of voltage sensors, which can be further adapted to other electrochemical devices.

SCADA Systems in Oil and Gas: Driving Innovation and Efficiency in the Digital Age

Abstract: In the oil and gas sector, supervisory control and data acquisition (SCADA) systems have become a revolutionary force that are transforming operations throughout the whole value chain. This in-depth analysis looks at the development, design, and various uses of SCADA systems in the upstream, middle, and downstream domains. It projects that the global SCADA market for oil and gas will grow to $4.52 billion by 2026. Real-time well monitoring, production optimization, and remote operations are made possible by SCADA systems in upstream operations, which greatly increase output and efficiency. The goal of midstream applications is to increase pipeline efficiency and safety by using predictive maintenance, enhanced leak detection, and flow optimization. By streamlining quality assurance, energy management, and process control, SCADA systems can completely transform refinery operations. SCADA deployments are not without difficulties, though, including data management complications, cybersecurity threats, and legacy system interoperability. In addition to addressing these problems, the study looks at new developments that could improve SCADA capabilities, such as edge computing, digital twins, AI and machine learning integration, and 5G technology. According to the research, SCADA is essential for promoting efficiency, safety, and innovation in the oil and gas industry. Businesses that successfully integrate SCADA technologies while overcoming implementation obstacles will be best positioned to prosper in the complicated and fiercely competitive energy market.

An intelligent solvent selection approach in carbon capturing process: A comparative study of machine learning multi-class classification models

Carbon capture is crucial for mitigating climate change and achieving global emissions reduction targets. Among various technologies, absorption-based methods using aqueous solvents are among the most common and efficient, offering high capture rates and retrofit capabilities for existing industrial facilities. However, selecting the optimal solvent is challenging due to its direct impact on the performance, efficiency, and cost-effectiveness of the carbon capture process. This research introduces a novel multi-class classification methodology for solvent selection in carbon capture. Leveraging a dataset of 656 data points, including features such as temperature, pressure, molar ratio, and CO2 solubility, the study employs nine machine learning algorithms—traditional models (Naïve Bayes, k-Nearest Neighbors, Decision Tree) and ensemble methods (Gradient Boosted Tree, Random Forest, Bagging, AdaBoost, Stacking, Voting)—to develop robust classification models. The results highlight the superior performance of the Stacking ensemble classifier, achieving 99.24 % accuracy, 0.76 % error rate, 99.04 % weighted mean recall, 98.08 % weighted mean precision, a 98.56 % F1 score, and a 0.9911 Cohen's Kappa coefficient on the test set. The effectiveness of ensemble techniques over traditional models underscores their ability to capture the complex relationships inherent in solvent selection. This approach streamlines the decision-making process for designers and engineers, enabling rapid identification of the most suitable solvent class based on operational conditions. Integrating this methodology into chemical engineering software could offer practical benefits, facilitating real-time solvent selection and optimization in industrial carbon capture, ultimately contributing to more sustainable and efficient processes.