Design Optimization Process Research Articles

Kinetic study of mineral oil removal from wastewater by the sono-electrochemical process

ABSTRACT Chemical kinetics can be a useful tool for determining the optimal operating time of electrochemical processes. The main objective of the study was to determine the mineral oil removal rate by sono-electrochemical treatment. In this study, zero-, first-, and second-order kinetic models were used to determine the reaction rate of mineral oil removal with the sono-electrochemical process. The reaction rate experiments were conducted under the following optimal conditions: 8 min of treatment time, a current density of 53.1 A/m2, and a flow rate of 0.23 L/s. It was found that the changes in mineral oil concentrations follow second-order kinetics with a coefficient of determination of 0.9732. The mineral oil removal efficiency was 94.4%. This study concludes that sono-electrochemical process could be a promising technology for the removal of mineral oil from wastewater, and that the mineral oil removal rate can be determined by chemical kinetics. The results obtained may be useful for the optimization of the sono-EC process and reactor design.

Multi-agent based optimal sizing of hybrid renewable energy systems and their significance in sustainable energy development

This paper delves into the enhancement and optimization of on-grid renewable energy systems using a variety of renewable energy sources, with a particular focus on large-scale applications designed to meet the energy demand of a certain load. As global concerns surrounding climate change continue to mount, the urgency of replacing traditional fossil fuel-based power generation with cleaner, more cost-effective and dependable alternatives becomes increasingly apparent. In this context, a comprehensive investigation is conducted on grid connected hybrid energy system that combines photovoltaic, wind, and fuel cell technologies. The study employs three state-of-the-art optimization algorithms, namely Walrus Optimization Algorithm (WaOA), Coati Optimization Algorithm (COA), and Osprey Optimization Algorithm (OOA) to determine the optimal system size and energy management strategies, all aimed at minimizing the cost of energy (COE) for grid-based electricity. The results of the optimization process are compared with the results obtained from the utilization of the Particle swarm optimization (PSO) and Grey Wolf optimizer (GWO). The findings of this study underscore both the practical feasibility and the critical importance of adopting on-grid renewable energy systems to decrease the dependence on traditional energy sources within the grid. The proposed WaOA succeeded to reach the optimal solution of the optimal design process with a COE of 0.51758129611 $//kwh while keeping the loss of power supply probability (LPSP), the reliability index, at 7.303681e-19. The practical recommendations and forward-looking insights provided within this research hold the potential to foster sustainable development and effectively mitigate carbon emissions in the future.

Design Method for Low-Ice-Class Propellers Based on Multi-Objective Optimization

The objective of this paper was to establish a comprehensive methodology for the optimized design of propellers for ice-class vessels, aiming to enhance hydrodynamic efficiency while ensuring structural integrity. This paper begins by introducing a novel approach for calculating blade stress, which takes into account both extreme ice loads and hydrodynamic loads, to be utilized in the propeller strength design process. Subsequently, a backpropagation (BP) neural network model was developed based on the data obtained from B-series propeller charts and integrated with a genetic algorithm to achieve a preliminary optimized design of the propeller’s hydrodynamic performance. To illustrate the application of this methodology, a case study of an ice-breaking tug propeller design is presented, detailing the optimization design process, including the preliminary, intermediate, and final design stages. The study also addresses key aspects such as geometric parameterization, the selection of optimization variables, the implementation of optimization algorithms, and the balance of multi-objective trade-offs. The proposed design approach can serve as a valuable reference for the practical engineering design of propellers for ice-class vessels, providing a systematic framework for achieving optimal performance in challenging operating conditions.

Critical construction waste minimization strategies for a circular economy in developing countries: A contractor’s perspective in China

AbstractDeveloping countries are often burdened by substantial construction waste (CW) generated through urbanization and urban renewal activities, highlighting the urgent need for effective CW minimization strategies to facilitate their transition towards a circular economy. Although previous studies have examined similar topics at various stages of construction projects from different perspectives, a comprehensive study integrating all critical stages from a contractor’s perspective is still lacking. To fill this gap, this study aims to identify critical CW minimization strategies in developing countries, with a holistic concentration on the planning, design, and construction stages, using China as a case study. æThe research began by compiling a comprehensive list of CW minimization strategies tailored to developing countries, based on an extensive desktop survey and a focus group interview, resulting in 32 strategies. A subsequent questionnaire survey with leading CW management experts and rigorous statistical analyses have identified 9 strategies as critical for minimizing CW in developing countries. Finally, through exploratory factor analysis, seven fundamental principles for CW minimization have been established: “Planning for CW Minimization” for the planning stage; “Optimized Design of Building Structures,” “Optimization of Design Process,” and “Stakeholders’ Efforts in the Design Stage” for the design stage; and “Optimization of Construction Techniques,” “Stakeholders’ Efforts in the Construction Stage,” and “Efforts on CW Disposal” for the construction stage. This study offers valuable insights for stakeholders in developing countries, empowering them to effectively minimize CW through targeted strategies, facilitating the transition to a circular economy and supporting the realization of the "zero-waste city" goal.

Hydrodynamic Shape Optimization of a Naval Destroyer by Machine Learning Methods

This paper explores the integration of advanced machine learning (ML) techniques within simulation-based design optimization (SBDO) processes for naval applications, focusing on the hydrodynamic shape optimization of the DTMB 5415 destroyer model. The use of unsupervised learning for design-space dimensionality reduction, combined with supervised learning through active learning-based multi-fidelity surrogate modeling, allows for significant improvements in computational efficiency while addressing complex, high-dimensional design spaces. By applying these ML techniques to both single- and multi-objective optimizations, aimed at minimizing resistance and enhancing seakeeping performance, the proposed framework demonstrates its practical value in hydrodynamic design. This approach provides a scalable and efficient solution, reducing the reliance on high-fidelity simulations while accelerating the optimization process, without substantial modifications to existing toolchains. A design-space dimensionality reduction of approximately 70% is achieved, reducing the design variables from 22 to 7 while retaining 95% of the original geometric variance. Additionally, computational cost reductions of 65% to 98% are observed, compared to using the full design space and high-fidelity simulations only.

Dynamic modeling of post-combustion carbon capture process based on multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder network

Solvent-based post-combustion carbon capture (PCC) technology is a promising, near-term solution for decarbonizing power generation and industrial facilities. Model-based process simulation is crucial for the optimal design and operation of the PCC process. Recently, data-driven models have gained attention due to their adaptability, efficient computation and high accuracy. However, the nonlinearity, strong couplings and multi-time scale features of the PCC process pose significant challenges for model identification. To this end, this paper proposes a multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder (MMoE-DAED) network for dynamic modeling of the PCC process under wide operating conditions. An encoder-decoder composed of long short-term memory (LSTM) network is employed to extract features from the time-dependent input data and learn the complex dynamic interactions caused by the inertial and delay properties of the process. Dual-stage attention mechanism is incorporated into the encoder and decoder respectively to select the most relevant input features and their correlations within the time series data. To enhance multi-output prediction accuracy, multi-gate mixture-of-experts (MMoE) framework that considers correlations of multitask learning is implemented. Simulation results using operating data from a PCC experimental setup indicate that the proposed modeling approach accurately predicts the steady-state values and dynamic trends of the CO2 capture rate and stripper bottom temperature over a wide operating range. The RMSE, MAPE and R2 indices for the CO2 capture rate are 2.1592, 0.0295, 0.9641, respectively, and for the stripper bottom temperature are 0.1491, 0.0003, 0.9833, respectively. Validations on a PCC simulator further verify the accuracy and efficiency of the MMoE-DAED model, which enables an 80.87% reduction in computation time compared to the simulator. This paper points to a new direction for the data-driven dynamic modeling of complex energy conversion processes.

Unusual intermediate layer precipitation in low-temperature salt bath nitrocarburized 316L austenitic stainless steel

This study focuses on investigating the precipitation behavior of the expanded austenite during low-temperature salt bath nitrocarburizing by adjusting the treatment temperature and time. The results indicate that the nitrocarburizing layer consists of a N-rich expanded austenite layer near the surface, and a C-rich expanded austenite layer closer to the substrate. Notably, all precipitates occur within the N-rich expanded austenite. When the temperature exceeds 430 °C, Cr3C2 and M2-3N emerge in the near-surface region. Furthermore, at 470 °C, with the prolongation of time, the M2-3N near the surface gradually decreases and even disappears, as the increase in carbon content within the N-rich expanded austenite enhances its stability. Specifically, at 430 °C, due to the high chemical potential of carbon and the nitrogen-induced lattice expansion, M5C2 forms near the interface between the N-rich layer and the C-rich layer. However, when the temperature rises to 470 °C, a significant amount of thermodynamically more stable M7C3 and M23C6 precipitates with the assistance of chromium diffusion. These findings may provide new insights for the process design and performance optimization of nitrocarburizing.

Optimization of Composite Wheel Fender for Manned Lunar Vehicle

Abstract Weight reduction is the eternal theme of aerospace optimization. In the process of weight reduction, the main ways are material change, structural change, and processing method change. In the process of aerospace structure design, the boundary and load conditions are more complicated, which restricts the development of optimization technology in the aerospace field to some extent. At present, the research object of optimization technology in the aerospace field is mainly the large parts of the aerospace craft, and the structural optimization of small aerospace parts is rarely involved. Therefore, the research of structural optimization design method has important theoretical and engineering significance. A complete optimization design process is proposed, the structural performance of aviation products is simulated by finite element software, and the feasibility of the optimization theory is verified. This article mainly compares several schemes for weight reduction design of composite material wheel fenders in the process of design.

Exploring co-creator’s experiences: a multiple case study addressing workplace sedentary behaviour

Abstract Co-creation is an innovative approach for addressing complex public health issues. Co-creator’s experience of participating in co-creation can impact engagement yet research investigating co-creation experience is limited. A better understanding of co-creation experiences can inform optimal design and implementation of the co-creation process. This study’s objective was to conduct a cross-case analysis of co-creator’s experiences from 3 co-creation processes aimed at addressing workplace sedentary behaviour within 3 different small-to-medium sized companies in Scotland. Thirty-one co-creators were involved in the case study companies from Oct 2022 to Jun 2023. Data sources included observations, interviews, and researchers’ reflections. A multiple case study design enabled the exploration of co-creator’s experience across cases. A within-case thematic analysis was conducted to develop a deep understanding of cases before comparing findings between cases using Miles and Huberman’s guide to cross-case analysis. Preliminary findings indicate co-creation experiences are associated with events occurring before, during and after the co-creation process and are influenced by company context (i.e., company structure, resources and demands) and co-creation context (i.e., design and facilitation of the process). Co-creators had various motivations for their involvement. During the co-creation process there was a range of common experiences such as a sense of belonging, enjoyment and frustration. After co-creation, there was an overall sense of satisfaction accompanied by positive outcomes, including an intention to adopt healthier behaviours. Insights into co-creator’s experiences support the use of co-creation for addressing workplace sedentary behaviour. The findings have implications for researchers planning co-creation processes at small-to-medium sized companies. Tailoring co-creation to co-creator’s needs and wider company context can enhance its innovative potential. Key messages • This study’s significance lies in its qualitative cross-case analysis of co-creation experiences which enhances research rigour and enables contextualised generalisation. • The innovative potential of co-creation can be enhanced by tailoring it to meet the needs and preferences of the co-creators and considering the wider company context.

Cost minimization of membrane-based separation systems for H2 recovery: A comparison of two optimization approaches

This paper addresses the optimization of membrane-based processes, with a specific focus on H2 recovery in hydrocarbon processing plants. A comparative analysis of two optimization approaches is conducted for designing H2 recovery systems for a multicomponent gas mixture containing H2/N2/CO2/CO. The main objective is to achieve the desired H2 recovery and product purity levels while minimizing costs. The paper compares two approaches for optimization: the first uses a simulation-based optimization technique with Aspen Custom Modeler/ASPEN Plus, while the second employs a simultaneous optimization method using GAMS (General Algebraic Modeling System). The methods are thoroughly compared, evaluating their strengths and weaknesses through qualitative and numerical assessments. To this end, a reference case was selected from the existing literature. The results obtained indicate that both proposed approaches are suitable for optimizing the design of a two-stage membrane configuration, as evidenced by the similarity of the optimal solutions obtained from both approaches. Subsequently, based on the analysis of numerical values for the optimal design problem for the investigated two-stage membrane configuration, a hybrid strategy for the optimal synthesis and design of multi-stage separation processes is proposed.The study fills a gap in the literature by providing a comprehensive analysis of the advantages and disadvantages of optimization approaches in the context of technology of separation of gases. This research provides valuable insights into the field and offers guidance for selecting appropriate approaches to improve the efficiency and cost-effectiveness of gas separation processes in real-world applications.

Practical implications of adding powdered activated carbon in advanced wastewater treatment for process and plant design

ABSTRACT The addition of powdered activated carbon (PAC) in advanced wastewater treatment is increasingly recognized for its effectiveness in removing micropollutants from secondary effluents. This study investigates the implications of PAC dosing on treatment performance, particularly in terms of natural and effluent organic matter (OM) removal, turbidity reduction, and sludge characteristics. Results indicate that while PAC incorporation significantly enhances the adsorption of OM, it necessitates higher coagulant dosages to achieve comparable or improved turbidity levels. The study also reveals that lower pH levels facilitate the removal of OM, thereby increasing the availability of adsorption sites on PAC for micropollutants. Moreover, PAC addition results in the formation of larger, denser flocs that settle faster, leading to a reduction in sludge volume by approximately 20%. However, the increased coagulant demand and the subsequent rise in sludge volume highlight the need for the optimized process design to balance treatment efficiency with operational costs. This research provides valuable insights for the design and operation of wastewater treatment plants, emphasizing the importance of tailored coagulant dosing and process adjustments when integrating PAC into existing treatment frameworks.

Multi-objective optimization of automotive seat frames using machine learning

The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a hybrid machine learning prediction model that combines the improved gray wolf optimizer (IGWO) and back propagation neural network (BPNN) has been proposed, and the prediction effect of the model was validated using the seat simulation data. Initially, based on the experimental data, finite element models were developed for eight typical working conditions of automobile seats and their accuracy was validated. Subsequently, the energy absorption to mass ratio method was employed to screen the design variables, resulting in the selection of 17 thickness variables and 15 material variables. Thereafter, the gray wolf optimizer (GWO) algorithm underwent enhancement through the incorporation of the dynamic leadership hierarchy (DLH) mechanism and the revision of the positional formula, yielding the IGWO algorithm. Following this, the IGWO algorithm was applied to optimize the hyperparameters of BPNN, culminating in the establishment of the IGWO-BPNN model. Ultimately, the seat multi-objective optimization design process was addressed using multi-objective gray wolf optimizer (MOGWO) to achieve the Pareto frontier, while the decision-making was conducted using the combined compromise solution (CoCoSo) method to determine the best trade-off solution. Furthermore, the effectiveness of the proposed optimal design method is evidenced by comparing the baseline design, simulation analysis, and optimal design methods. The results indicate that the optimized automotive seat frame achieves a reduction in cost by 20.7 % and mass by 22.9 %, simultaneously maintaining safety performance. Consequently, the proposed optimization design methodology is demonstrated to be highly effective for the multi-objective optimization design of automotive seat frames.

Research on multi-objective energy-saving optimization of propylene distillation column

The energy-saving optimization problem of the propylene distillation column was investigated. A PRO/II process simulation model was established based on the actual operating data of the unit and a sensitivity analysis of the operating parameters was conducted. On this basis, a multi-objective optimization model for the unit was established using regression analysis. The penalty function mechanism and dynamic search factor were incorporated into the MOGWO algorithm to solve the optimization model. The optimization results demonstrate that by optimizing multiple sets of operating parameters, the propylene distillation column can increase its production while reducing its energy consumption, achieving energy-saving optimization for the unit. This method provides a new theoretical basis for the energy-saving optimization design of propylene distillation units and other distillation processes.

Thermodynamic modelling of systems involved in natural gas dehydration with triethylene glycol using a group contribution association model

Natural gas (NG) dehydration through absorption into Triethylene Glycol (TEG) is one of the most important applications in the NG industry. The optimal design of the TEG dehydration process requires a deep understanding of the thermodynamic behavior of mixtures containing TEG, water, hydrocarbons, and other compounds present in natural gas. In this work, the recently developed Universal Mixing Rule – Cubic Plus Association (UMR-CPA) group contribution equation of state (EoS) is extended to these systems. UMR-CPA combines the PR-CPA EoS with the UNIFAC group contribution activity coefficient model through the Universal Mixing Rules. Parameters for pure water, TEG and NG components were determined by accurately fitting vapor pressure, density and heat capacity data. For non-associating compounds, the model leads to overall deviations of 1.2 % in vapor pressures and 6.1 % in isobaric heat capacities. Water properties are also quite accurately described, with overall deviations of approximately 0.4 %, 1.2 % and 5.7 % in vapor pressures, liquid densities and isobaric heat capacities, respectively. The model was then applied to mixtures of water and TEG with gases and hydrocarbons by correlating the proper group interaction parameters. Very satisfactory results were obtained for both vapor-liquid and liquid-liquid phase equilibria in these systems, where also an adequate reproduction of the minimum of hydrocarbon solubility in water was noted. Finally, the UMR-CPA EoS was further validated through the prediction of the phase behavior of ternary systems including TEG and/or water and NG compounds. Very good predictions were achieved for the low TEG and water content in the vapor phase of the TEG-H2O-CH4 ternary system, with absolute deviations of around 0.05 and 23.26 ppm, respectively. Overall, the model yields accurate predictions, suggesting its suitability for designing the TEG dehydration process.

An efficient framework for design optimization using parametric reduced order modeling in a virtual prototyping phase

Virtual Prototype Assembly is a tool enabling the virtual assembly of components, the prediction of noise and vibration performance, and the evaluation of component modification scenarios. FRF-based substructuring is the technique used to assemble the components, either obtained from experimental measurements or numerical simulation models. However, numerical simulations of components can be time-consuming, especially when exploring several component design modifications of large models. The paper proposes a framework combining Virtual Prototype Assembly with a parametric Model Order Reduction technique for vehicle design optimization. The proposed parametric Model Order Reduction technique generates a Reduced Order Model of a component maintaining an explicit dependency on material parameters and properties, such as: Young's Modulus, density, Poisson coefficient, connection stiffness, etc.. This enables fast and efficient simulation of components designs without the need of recomputing numerical models, thus streamlining the component design optimization process. The effect of component design modifications will be assessed at the predicted target vibration levels on an academic application case.

Innovative simplified approach and numerical investigation for flow and heat transfer in internally threaded tubes

Internally threaded tubes exhibit exceptional heat transfer capabilities, holding paramount significance in enhancing energy efficiency, reducing energy consumption, and tackling thermal challenges across aerospace, automotive, and environmental domains. Nonetheless, their intricate structural features, software modelling intricacies, extensive grid requirements, and challenges in maintaining grid quality render numerical simulation investigations a demanding endeavor. This study elucidates the influence mechanisms of thread spacing on the distribution of single-phase and two-phase flows. It delves into the condensation heat transfer of the R32 refrigerant and the single-phase heat transfer of water within internally threaded tubes. At the same Reynolds number, the heat transfer rate can be improved by approximately 19.61% with the optimized internally threaded parameters. When the Reynolds number varies from 18,626 to 51,222, the heat transfer rate per unit length for Tube-5 remains around 2,478.9 W/m, with a variation of less than 5.19%. Furthermore, it presents a simplified numerical simulation approach tailored for internally threaded pipes. This method manipulates wall roughness and regulates wall rotation speed to mimic fluid dynamics phenomena akin to conventional numerical simulation techniques. The findings reveal that the proposed simplified numerical simulation approach yields heat transfer and pressure drop predictions with less than 3% discrepancies compared to traditional numerical simulation predictions. Simultaneously, compared to directly simulating threaded pipes through conventional means, this approach significantly mitigates grid generation complexities, slashing computational time costs by at least 85% and facilitating efficient thermal design and structural optimization processes.

Improved black-winged kite algorithm and finite element analysis for robot parallel gripper design

This paper presents a comprehensive study on the design optimization of a robotic gripper, focusing on both the gripper modeling and the optimization of its parallel mechanism structure. This study integrates the Black-winged Kite Algorithm (BKA), Finite Element Analysis (FEA), Backpropagation Neural Network (BPNN), and response surface optimization techniques. The Good Point Set (GPS), nonlinear convergence factor, and adaptive t-distribution method improve BKA, which enhances exploration and exploitation performance, convergence speed, and solution quality. Subsequently, the parallel mechanism structure is designed to minimize the total mass, total deformation, and maximum equivalent stress. The central composite design (CCD) method was used to design the FEA experiment and establish the BKA-BPNN regression prediction model. The RMSE of this model’s training set and test set are 0.001615 and 0.0029328. A response surface optimization model is constructed to determine the best design solution. The optimized design achieves a 33.12% reduction in maximum equivalent stress, a 1.47% decrease in total mass, and a 0.16% reduction in maximum total deformation. This study provides valuable insights into the design optimization process for robotic grippers, showcasing the effectiveness of the proposed methodologies in enhancing performance while reducing mass and improving structural integrity.

Multi-objective Topology Optimization Design for a Certain Launcher Bracket

To achieve weight reduction and enhance the firing accuracy of a specific type of launch device, the bracket was selected as the optimization subject for multi-objective topology optimization. Single-objective optimization often overlooks other influencing factors. To address the limitations of single-objective optimization, this study adopts the variable density method from the SIMP approach and proposes a multi-objective topology optimization based on compromise programming. This study, through multi-objective topology optimization of the bracket, obtained an optimized topology structure that maximizes static stiffness and the dynamic low-order natural frequencies of the launch device bracket at launch angles of 0°, 53°, and 85°, with an azimuth angle of 0°. Finally, the obtained topology structure was validated using finite element software. The design method presented in this paper not only enhanced the stiffness of the bracket structure for such launch devices and increased the first two natural frequencies of the bracket but also achieved weight reduction. The optimization design process also provides a reference for other mechanical structures.

Techno-economic design and placement tool for energy recovering pressure regulating turbines in water distribution systems

Water distribution systems present poor energy efficiency due to the significant amount of energy that dissipates in the form of excess water pressure. Besides energy losses, excess pressure increases water losses due to leakages and may result in pipeline damage. The employment of micro-turbines that concurrently harness excess energy and achieve pressure management is proposed by many researchers as potential replacements to the currently used pressure reduction valves. This work relies on previous work that determines the optimal design and operating point of such turbines integrated in the power distribution system, in order to develop an algorithm that considers the economic viability of such projects. The suggested algorithm has been applied to a simulated large-scale WDS in Kentucky that contains several locations where pressure regulation is currently performed or planned due to immense local pressure. Involving the economic viability of pressure regulating turbines within the design optimization process improves pay-back period and levelized cost of energy by more than 10 % and 20 %, respectively, compared to when their design is optimized solely for pressure regulation and maximum energy yield. The application of this approach to a typical WDS allowed more than 161 MWh/year of clean energy to be produced.

Fatigue Life and Stress Analysis of a Single Cylinder Four Stroke Crankshaft

This paper focuses on the optimization of a crankshaft using ANSYS software in terms of weight and strength. The initial designs of the crankshaft, piston, and connecting rod were created using SolidWorks. The force generated by the gas during the combustion process was calculated to be 12017 N. Next, the SolidWorks assembled system was imported into ADAMS View software for simulation, which revealed a time-variant force of the crankpin of 14049 N. The calculated value was verified with results obtained from analytical calculations, showing a deviation of 0.23%. Finite element analysis was done for the crankshaft using ANSYS transient structural after applying loadings and boundary conditions. The optimization process aimed to minimize the crankshaft's weight while maintaining its strength and durability. The results of the ANSYS simulations showed a weight reduction of 2.5% from the original 2.983 kg to 2.907 kg, while maintaining the required strength and durability. The optimized crankshaft was compared to its original design in terms of fatigue life, weights, and stresses. The maximum von Mises stress was reduced by 16%, shear stress by 3.5%, and deformation by 3.5%, which were validated through analytical calculations. The crankshaft analysis resulted in a significant increase in fatigue life, calculated to be infinite under the given conditions. To conclude, the objective to optimize the crankshaft for performance and efficiency was achieved, demonstrating a 2.5% weight reduction and substantial improvements in fatigue life and stress distribution, proving the effectiveness of ANSYS software for the design optimization process.