Design Optimization Process Research Articles

Model Adequacy in Assessing the Predictive Performance of Regression Models in Pharmaceutical Product Optimization: The Bedaquiline Solid Lipid Nanoparticle Example

This study aimed to assess the predictive performance of first- and second-order regression models in optimizing bedaquiline (BQ) solid lipid nanoparticle (SLN) formulations. A three-step central composite design and graphical optimization process was employed. A design of experiments method was used to evaluate the impact of BQ, Tween 80 (T80), polyethylene glycol (PEG), and lecithin on the formulations’ response variables, including Z-average (PSD), polydispersibility index (PdI), and Zeta potential (ZP). Secondly, we quantified the relationship between experimental variables using the regression model coefficients. Lastly, we predicted the responses and verified the models’ adequacies to ensure accurate representation and effective optimization. The first-order polynomial showed poor model adequacy and required further refinement due to its lack of explanatory power and significant predictors. Conversely, the second-order models provided superior fitness, sensitivity to variability, complexity, and prediction consistency. The optimized formulation achieved a desirability value of 0.9998, indicating alignment with the desired criteria. Specifically, the levels of BQ (19.4 mg), T80 (25.2 mg), PEG (39.2 mg), and lecithin (200 mg) corresponded to PdI (0.41), PSD (250.99 nm), and ZP (−25.95 mV). Maintaining a BQ concentration between 10 and 20% and T80 between 15 and 18% is vital for maximizing ZP and minimizing PdI and PSD, ensuring stable SLN formulations. This study underscores the significance of precise model selection and statistical analysis in pharmaceutical formulation optimization for enhanced drug delivery systems.

Risk−Based Cost−Benefit Optimization Design for Steel Frame Structures to Resist Progressive Collapse

The design of structures to resist progressive collapse primarily focuses on enhancing structural safety and robustness. However, given the low probability of accidental events, such designs often lead to a negative cost–benefit. To address this problem, this paper uses risk analysis to optimize the progressive collapse resistance design of steel frame structures. The elements’ cross-section design for the progressive collapse resistance of steel frame structures is optimized using genetic algorithms and SAP2000 23, which identify the structural model with the minimum robustness index while ensuring safety. The results show that the risk-based robustness index can effectively assess the cost of progressive collapse design. More importantly, the optimization model can rapidly identify the most cost-effective structural design solution that complies with progressive collapse resistance guidelines, enhancing the simplicity and usability of the structure design optimization process. Additionally, the integration of the SAP2000 API with Python 3.8 automation streamlines the parameterization process, minimizes manual errors, and enhances the precision and efficiency of the structural design optimization. Finally, the model’s effectiveness is validated through a case study, where the refined single-frame structure shows a reduction in initial construction and collapse-related costs by 2.4% and 9.1%, respectively. Meanwhile, the three-dimensional frame shows a 2.9% rise in initial costs but a 13.5% decrease in total collapse-resistant design costs, illustrating the model’s ability to balance safety with cost-effectiveness.

Optimization of Process Design and Operating Parameters of H2S Removal Unit to Reduce Lean Amine Inlet Temperature of Amine Contactor at Upstream Oil and Gas Subsidiary SI

Upstream Oil and Gas Subsidiary SI is a natural gas processing company that operates an H2S removal unit to convert natural gas rich in CO2 and H2S into sweet gas. The main problem of this unit is the high temperature of lean amine entering the Amine Contactor. The purpose of this study is to determine the factors of high lean amine temperatures, evaluate the lean amine cooler, amine regenerator overhead cooler, and plate exchanger performance, and determine the optimal process design configuration and operating parameters. The method used is the simulation of the H2S removal unit with Aspen HYSYS, followed by a comparative analysis between simulation data and equipment design data. The independent variables of this study include Reboiler duty, reflux ratio, and heat transfer area in the Plate Exchanger, with the main dependent variable being lean amine temperature. The results showed that the high lean amine temperature was caused by a decrease in the performance of the Lean Amine cooler and amine regenerator overhead cooler, as seen from the UA and LMTD values of the simulation results which were smaller than the design. In contrast, the Plate Exchanger still functions well with a UA value greater than the design. Optimization was carried out by adjusting the process design and operating parameters of the H2S removal unit. The optimized design involves bypass reflux from the regenerator to the rich amine stream entering the rich/lean amine exchanger and increasing the heat transfer surface area of the plate exchanger to 62.17 m². The influential operating parameters are reboiler duty, liquid flow rate to the mixer, and plate exchanger heat transfer surface area. Optimal operating conditions were achieved at a Reboiler duty of 1,642 kW, a liquid flow rate of 1.4 m³/h, the reflux to Mixer ratio is 100%, and a heat transfer surface area of 62.17 m². In addition, it can be concluded that the optimization of process design and operating parameters successfully reduced the lean amine inlet temperature of the Amine Contactor.

Optimizing Perforation Phasing through Nearest Neighbor Lookup and Delaunay Triangulation

Summary Sand production is a common occurrence in reservoirs with weak sandstone undergoing pressure depletion during hydrocarbon extraction. Among available techniques, the adoption of the cased, cemented, and perforated well completions (CCPs) stands out for its inherent safety measures and cost-effectiveness compared to alternative methods. This approach is anticipated to offer substantial potential for bolstering hydrocarbon yields when juxtaposed with the openhole completion methodology, positioning it as a preferable well completion strategy for such reservoirs. In the design of perforation arrangements, the determination of shots per linear foot (SPF, spf) is typically contingent upon the targeted hydrocarbon production capacity. Thus, fine-tuning the phase angle becomes imperative to forestall overlap within the damaged zones surrounding neighboring perforations, thereby mitigating the interaction between them—a pivotal consideration in the design optimization process. In light of the absence of constraints and the relative ease of implementing a single helical perforation pattern (SHPP) over other intricate designs, this research is centered on ascertaining the minimal separation between adjacent perforations, termed the minimum perforation-to-perforation spacing (PPSm), across various phase angles within this pattern. This objective is pursued through a swift exploration of nearest neighbors (NN) facilitated by a spatial-partitioning data structure, specifically a k-dimensional tree (KDTree), aimed at probing the optimal phase angles for customary values of SPF, predicated on a well diameter of 8½ in. Achieving a uniform distribution of the perforations has been realized through the application of mathematical and statistical principles, utilizing a parameter known as the equilateral likeness score (ELS). Employing the Delaunay triangulation, the arrangement of the perforations has been meticulously configured to minimize this score, thereby signifying the most even distribution of them. The optimal phase angles have been discerned from two distinct vantage points—maximizing the spacing between adjacent perforations and maximizing the feasible angle values for the phasing. A comparative analysis between the outcomes derived from the proposed theory and those from prior theories—including the isosceles obtuse triangle pattern (IOTP)—reveals the likelihood of notable disparities in the calculated optimal phase angles. Particularly within the realm of the two-wrapped-based methodologies, variations in the magnitude of these angles can be substantial, extending up to approximately 80° for certain shot densities. Notably, the optimal phase angles of 97° and 77° have been estimated for two prevalent shot densities of 6 spf and 12 spf, respectively, contrasting with the values of 99° and 143° previously obtained using IOTP. This substantial difference in phase angles observed for the shot density of 12 spf has been minimized to approximately 1° through refinement of the previous method, achieved by removing its constraint on the number of wraps.

Completion and Stimulation Design Evolution in Tight Chalk Formations in Offshore Norway

Summary Lower completion and acid stimulation designs for low-permeability chalk formations in a series of North Sea fields were modified from historical/legacy approaches to improve well performance for both production and injection purposes. The modifications included (1) a stimulation change from high-rate matrix acidizing to acid fracturing and (2) optimization of ball-activated sliding sleeve designs. The improvement in well performance was validated by actual productivity comparisons to offset wells. Further improvements are being developed for future extended reach wells. A ball-activated sliding sleeve completion was chosen for a new series of improved horizontal well completions. Sleeve spacing, the number of sleeves, and port sizes were subsequently optimized for targeted stimulated lateral lengths by pipe flow modeling and limited-entry design methods. Optimization of acid fracturing designs was achieved after incorporating critical findings from various laboratory tests including rotating disk tests, acid-etched fracture conductivity tests, and gel shear history simulator tests. The new acid fracturing treatment designs were generated with the help of numerical simulations that were continuously fine-tuned based on new observations made during treatments and rigorous analysis of bottomhole injection pressures during the treatment. As a result of the lower completion design optimization process, different-size nozzles were introduced into the sliding sleeves to treat up to five sleeves per stimulation stage with effective, limited-entry, fluid diversion. This allowed (1) use of available pumping weather windows effectively in the often-challenging North Sea offshore environment, (2) reducing or eliminating time-consuming wireline perforation runs, and (3) limiting acid exposure to mitigate ball/seat zonal isolation loss events (i.e., dissolvable balls were not always compatible with acid). The new and improved acid fracturing design enabled a reduction of the number of gel/acid cycles from five to three, which reduced stimulation cost without losing stimulation effectiveness. The new design also resulted in productivity enhancements depending on the well location within the structure. The largest performance improvement was observed in wells that were placed farther downflank, where stronger reservoir rock and lower permeabilities were found. These wells required a more intensive acid fracturing treatment to generate economical and sustainable production rates. Finally, wells that used the new stimulation techniques also tended to require a lower restimulation frequency. This type of analysis work has not been presented previously and it optimizes lower completion for acid fracturing stimulation on a well-by-well basis. Further improvement is expected as stimulation designs and completion technology continue to evolve.

Construction Strategies for Residential High Rise Buildings Decoration during Structure Work

The market's attention to the quality and efficiency of engineering construction is continuously increasing. Quality inspection departments, market departments, and others have begun strict supervision and review of projects, aiming to ensure that they are completed in accordance with the correct ideas and standards. Process interleaving is achieved through design optimization and process optimization, reducing processes and measures, and fully utilizing spatial and process parameters to achieve the goals of shortening time parameters, improving quality, and reducing costs. The design of process interleaving schemes lies in the effective utilization of the plane site and spatial resources on the construction site, maintaining the improvement of utilization efficiency, installing as many construction mechanization equipment as possible, and adopting an automated construction mode to improve not only construction efficiency but also reduce the impact of external temperature, humidity, and climate, thereby achieving better improvement in engineering construction quality.

РОЛЬ ТЕХНОЛОГІЙ БУДІВЕЛЬНОГО ІНФОРМАЦІЙНОГО МОДЕЛЮВАННЯ В АРХІТЕКТУРНІЙ ОСВІТІ

The article provides a detailed analysis of the role of building information technologies in the professional training of first-level (bachelor’s) degree students in the 191 "Architecture and Urban Planning" program at the Institute of Architecture and Design, Lviv Polytechnic National University. Special attention is paid to the study of the role of modern technologies in shaping the key skills and professional competencies of future architects. In particular, the integration of Building Information Modeling (BIM) and a wide range of other licensed software tools, which are actively used both in the professional field and in the educational process, is examined. The article explores the trends regarding the growing demand for specialized software among employers in the architectural labor market. It also considers the main differences between traditional architectural design methods, based on planar drawings and conceptual sketches, and modern information-based approaches that use digital tools to ensure precision, efficiency, and optimization of design processes. The significance of BIM technologies as one of the key aspects of the digital transformation in the architectural and engineering design process is emphasized. The article also analyzes methodological approaches to forming learning outcomes and developing specialized competencies necessary for preparing competitive professionals in the field. Significant attention is given to the challenges faced by instructors and students when implementing BIM technologies into the educational process. The article discusses potential obstacles and risks associated with the integration of modern digital technologies into the academic curriculum, as well as possible ways to overcome these challenges.

A novel machine learning workflow to optimize cooling devices grounded in solid-state physics

Cooling devices grounded in solid-state physics are promising candidates for integrated-chip nanocooling applications. These devices are modeled by coupling the quantum non-equilibirum Green’s function for electrons with the heat equation (NEGF+H), which allows to accurately describe the energetic and thermal properties. We propose a novel machine learning (ML) workflow to accelerate the design optimization process of these cooling devices, alleviating the high computational demands of NEGF+H. This methodology, trained with NEGF+H data, obtains the optimum heterostructure designs that provide the best trade-off between the cooling power of the lattice (CP) and the electron temperature (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\ ext{T}}_{e} $$\\end{document}). Using a vast search space of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.18 \ imes 10^{-5}$$\\end{document} different device configurations, we obtained a set of optimum devices with prediction relative errors lower than \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${4}\\,\\%$$\\end{document} for CP and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${1}\\,\\%$$\\end{document} for Te. The ML workflow reduces the computational resources needed, from two days for a single NEGF+H simulation to 10 s to find the optimum designs.

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.

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.

Design and Optimization of a Piecewise Flight Controller for VTOL UAVs

The dynamic systems of unmanned aerial vehicles (UAVs) are susceptible to parametric uncertainties, unmodeled dynamics, and external vibrational disturbances during motion control. Although proportional–integral–derivative (PID) controllers are widely used in UAVs, the manual tuning process is time-consuming, and the resultant control performance is vulnerable to uncertainties in varying flight conditions, leading to suboptimal flight control throughout the entire hovering flight envelope. To address this issue, the present study designs and optimizes a novel piecewise flight controller based on a hybrid data-driven approach combining gradient-based methods and pattern search algorithms. The optimization process is validated through comprehensive flight testing, comparing the piecewise controller with the baseline controller across a wide range of flight conditions. The study examines the objective functions and design parameters, demonstrating the robustness of the optimized piecewise controller in the presence of disturbances. The results reveal significant variations in the system model under different flight conditions, and the piecewise optimized controller demonstrates a response that is more than twice as fast as the benchmark, with acceptable overshoots and steady-state errors under the predefined trajectories. The salient features of the proposed piecewise flight controller and design optimization process are verified through flight testing, which is expected to provide a framework for future UAV flight control designs.

Performance of a novel annular electric field membrane bioreactor and its membrane fouling control in treating catering wastewater

This study aimed to investigate the effects of different voltage and aeration conditions on catering wastewater treatment and membrane fouling in a novel annular electric field membrane bioreactor (AEMBR). The results indicated that the synergistic effect of annular electric field and aeration promoted the degradation of wastewater and the alleviation of membrane fouling. The treatment effect was optimal under a micro electric field of 0.5 V, with removal rates for COD, NH4+-N, TP, and oil ranging from 96.85% to 99.36%, 80.43%–83.01%, 95.46%–97.79%, and 98.83%–99.15%, respectively. Additionally, the fluorescence intensity of macromolecular proteins and small molecular acids decreased. Simultaneously, the average growth rate of transmembrane pressure (TMP) reduced by approximately 0.4 kPa/d. The species abundance and diversity of activated sludge increased, promoting the growth of dominant bacteria, all while maintaining low energy consumption. The aeration intensity had relatively little impact on system operation, and the force of the annular electric field was greater than the force of aeration. This study verified the optimal benefits under micro electric field conditions and provided a basis for the optimization of future process design to achieve a more efficient and economical wastewater treatment system.

Design and Rapid Optimization Procedure of a Compact Broadband Microstrip Diplexer for VHF/UHF Band

ABSTRACTIn this paper, a design procedure of a compact broadband microstrip diplexer working on the Very High Frequency (VHF)/Ultrahigh Frequency (UHF) band is proposed. The diplexer consists of a fourth‐order Butterworth Low‐Pass Filter (LPF) and a sixth‐order elliptic High‐Pass Filter (HPF). The two filters are located on two substrates with a common ground. A microstrip‐to‐coaxial structure is utilized to transmit the power flow between the two substrates. For miniaturization, the utilized ideal capacitors and inductors are realized by the commercial lumped‐element capacitors and the curved microstrip line sections, respectively. All the middle design and rapid optimization processes from the schematic simulation by Advanced Design System (ADS) to the circuit simulation by CST STUDIO SUITE (CST) are given in the article. A prototype is fabricated and measured. The testing results are consistent with the simulation. The diplexer can operate at 225–775 and 880–2500 MHz. The insertion losses are 0.8 ± 0.3 and 0.9 ± 0.4 dB in the two working bandwidths. The isolation bandwidth of −20 dB level is as small as 105 MHz. The roll‐off is also sharp that the related bandwidths corresponding to the attenuation level −3 to −20d B are 90 and 30 MHz to the high and low bands, respectively. Besides, the return loss and isolation are larger than 10 and 20 dB approximately. The diplexer has a volume of 0.065 λg × 0.156 λg @775 MHz, which can be utilized to feed the miniaturized broadband VHF/UHF antenna system.

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.