Design Optimization Process Research Articles

Optimization of routing problems in manufacturing supply

The optimal design of a suitable material supply process is important for the continuous, smooth operation of manufacturing processes, which includes the transportation and handling of work pieces, components, tools, equipment, pallets, packaging materials, and measuring instruments. The most common form of implementation of these material handling processes, especially in mass production, is the milkrun supply. In this paper, the author presents a method to optimise the milkrun material supply processes in manufacturing. The author gives a brief overview of research results related to milkrun-based material supply solutions. An Excel Solver-based optimization method is presented that is suitable to support the design of an optimal material supply process in any manufacturing environment by determining optimal material supply routes and optimal number of milkrun trolleys, depending on the supply demand, location of warehouse and manufacturing cells and the capacity of the milkrun trolleys.

Predicting steel column stability with uncertain initial defects using bayesian deep learning

The stability of steel columns is difficult to predict accurately due to uncertain initial defects such as geometric imperfections and residual stress. To address this issue, we propose a probabilistic model that uses variational autoencoder (VAE) and transfer learning to estimate the loading capacities of steel columns. Our model can predict the confidence intervals of buckling loads without knowing the exact distribution of initial defects, providing more comprehensive information for engineering applications than traditional deterministic strength index. We establish a dataset of 1500 load-displacement curves of steel columns using four data augmentation approaches, and analyze the data distribution to validate the model's assumptions. Various criterions, including the mean squared error (MSE), the prediction interval coverage probability (PICP), and the prediction interval normalized average width (PINAW), are adopted to comprehensively measure the performance of confidence interval prediction. The numerical experiment validates that the trained model accurately predicts the confidence intervals for load-displacement responses, which perfectly cover the true curves with reasonable PINAW. Finally, we conduct a case study with a practical experiment to illustrate the model's potential application in failure probability calculation and reliability design. Our proposed model provides a promising probabilistic solution for quantifying the impact of uncertain parameters on structural analysis and significantly simplifies probability-based reliability design and optimal design processes.

Algorithmic model generation for multi-site multi-period planning of clean processes by P-graphs

Optimal clean process design requires strict constraints to enforce waste and byproduct management, all of which can be formulated in the language of mathematical programming. However, waste management and the utilization of by-products are often carried out in locations or periods other than the production process. The paper describes all modeling steps by P-graphs sufficient to represent raw material availability and production capacities in multiple time periods at multiple sites, as well as transportation and storage capacities of process materials and wastes. These steps are integrated into a single comprehensive model generation algorithm. For easier understanding, each model generation step is illustrated by a case study of planning a multi-site multi-period furniture production process alongside the recent challenges of energy supply and waste management. Finally, the case study of furniture production is analyzed under various circumstances to highlight the power of the proposed tools in daily production and transportation planning. Accordingly, the proposed method provides such alternative 5 best manufacturing and logistics plans that, in the event of a complete failure or overloading of one of the production capacities at either locations, there is still an alternative plan within a 3% profit decrease.

Enhancement of ultrasonic-assisted extraction and antioxidant potential of phenolic compounds from Moroccan Cannabis sativa: A green intelligent approach integrating optimal mixture process design and artificial neural networks

Environmentally-friendly extraction methods enables both efficient extraction of phenolic compounds from plant sources and safe use of the extracted substances. These characteristics render them as ideal choices for sustainable extraction processes. This study combines a green and sustainable extraction strategy with an intelligent statistical approach to optimize the recovery and the antioxidant activity of phytochemicals derived from Moroccan Cannabis sativa. The optimization of the ultrasound-assisted extraction is achieved using an optimal mixture-process design (OMPD) combined with artificial neural networks (ANNs). The OMPD consists of a mixture of water, glycerol, and ethanol, along with process parameters such as processing time, solvent-to-material (S/M) ratio, and temperature. The objective is to optimize the five responses extraction yield, total phenolic content (TPC), and antioxidant activities measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH), total antioxidant capacity (TAC), and Ferric reducing antioxidant power (FRAP) assays. The results of the OMPD indicate that the recovery of phenolic compounds and antioxidant activity can be improved by using a binary mixture of water and ethanol, along with moderate processing time and temperature, and a high S/M ratio. The optimal responses were 26.06 %, 92.6 mg GAE/g, 0.1 mg/ml, 298 mg AAE/g, and 1.3 mg/ml for yield, TPC, TFC, DPPHIC50, TAC, and FRAPEC50, respectively. Subsequently, the OMPD matrix was used as input for the ANNs, employing a 6-6-5 multi-layer perceptron design. The findings of the ANNs closely matched those obtained from the OMPD models. However, the performance parameters of the ANNs models were slightly superior to those of the OMPD.

Periodic motion and optimization design of non‐stacked particle damper based on the double inert mass equivalent model

AbstractTo promote the application of non‐stacked particle damper (NSPD) in civil engineering, a design methodology was suggested on the basis of the double inert mass equivalent model. NSPD single‐story steel frame model is selected as the research object in this paper. First, the analytical solution for the periodic motion with symmetric two impacts was discussed. The shaking table test was performed to verify the rationality of the analytical solution under harmonic excitation. Subsequently, based on the analytical solution, the influence of equivalent connection stiffness on the vibration reduction effect and mechanism was analysed under resonant and non‐resonant harmonic excitation. The numerical analysis methods of the optimal equivalent connection stiffness were obtained accordingly. Finally, the effectiveness and generalization of the proposed optimal equivalent connection stiffness were discussed numerically under harmonic excitation and seismic ground motion. An optimal design process for the NSPD was proposed accordingly. Based on this, the vibration reduction performance analysis of NSPD under far‐field records, near‐fault pulse‐like records and near‐fault non‐pulse records was further conducted in the end. The results show that the time‐dependent frequency behaviour of the uncontrolled structure has a significant impact on the performance of the NSPD. The proposed optimization method is effective, which can be used as the foundation for the optimization of NSPD under earthquakes.

Simulation of spiral-wound pressure retarded osmosis for harvesting osmotic power: Module-level modeling and implications of feed pre-treatment

A process-scale model for pressure retarded osmosis (PRO) using a 1-dimensional representation of the spiral-wound membrane is presented. Comparing a length-averaged modeling approach with a true counter-current approach reveals that the former holds only for relatively short membrane lengths while it over-predicts the net power output for longer membranes. For a membrane line with eight spiral-wound elements in series, the length-average model over-predicted the PRO performance by 20 %. The model was further used to assess the impact of feed pre-treatment on process performance and optimal process design. Feed pre-treatment was found to be a dominant factor affecting the optimal inlet feed flow rate and the power output. Our model suggests that for a moderate to high feed pre-treatment requirement the inlet feed flow rate is close to minimum feed flow rate of the membrane module. In the absence of feed pre-treatment a power density of 143 and 284 W m−2 for CTA and TFC membranes, respectively, was found. When feed pre-treatment with 100 Wh m−3 was employed these numbers dropped to 48 and 94 W m−2.

Quality improvement of food products using taguchi method: a study in a bread product SME

Quality is one of the most crucial factors for manufactured products. Good product quality will increase customer loyalty and hence, higher profit for the company. SR company is a local Small Medium Enterprise (SME) in Bandung that produces bread products. Among various kinds of bread, Kadet is the main product with an average production of approximately 1000 pcs per day. In addition, Kadet bread has numerous defective products with an average of 3%-5% per 1000 pcs. Moreover, Kadet is a product baked at high temperatures, and hence, the timing of the production staff is crucial to prevent defectiveness. In this work, we use the Taguchi method to obtain factors that produce defectiveness and the combination of the optimal production factors. Furthermore, using the Analysis of Variance, the results show the optimal process design for minimum defective Kadet bread products as the process evaluation for better product quality.

First principle based rate equation (1pRE) for random pore model in Fe2O3 reduction reaction with CO

AbstractThe redox kinetics of the oxygen carriers are crucial in Chemical‐Looping Combustion (CLC) systems. The random pore model (RPM) was widely used to investigate the macroscale redox kinetics of oxygen carriers in previous studies, in which the reaction rate constants were obtained just by fitting the experimental data. This study presents a first principle based rate equation (1pRE) applied to the RPM to calculate the reaction rate constants directly using density functional theory (DFT). The 1pRE is integrated with the RPM that accounts for the effect of pore size distribution derived from the surface area evolution of reaction interface and product layer diffusion, thus bridging the gap between the microscale elementary surface reactions and macroscale overall conversion. The developed 1pRE predicts the reduction kinetics of Fe2O3 oxygen carriers accurately, thereby facilitating the optimization of design processes for oxygen carrier materials and the scale up of CLC reactors.

Effects of the burner on SiO2 formation and deposition in the OVD process

The burner of outside vapor deposition (OVD) is of great significance for enhancing the yields and quality of the optical fiber preform. This study aims to numerically investigate the effects of the burner on silica particles formation and its corresponding deposition performance in the OVD process. This work is based on our recently developed integrated Eulerian-Eulerian multiphase OVD process model combined with a particle nucleation model. The effects of the distance between the burner and target, the horizontal offset of the target to the burner, the shape of burners, swirl burners as well as double burners are investigated. The results demonstrate that there exists an optimal value for the distance between the burner and target to yield the largest deposition efficiency. However, the deposition efficiency monotonically declines as the horizontal offset of the target to the burner increases. The elliptic burner potentially provides a better match with the target surface for particle deposition. An optimal number of helical vanes installed inside the O2–1 inlet is observed in the counter-swirling burner of O2–1 and H2 inlets, which yields the largest deposition efficiency of 33.4%. Double burners could promote the deposition rate, but could not enhance the particle deposition efficiency. The results provide a deeper understanding of the burner design and OVD process optimization.

A Study on the Optimal Design Method for Star-Shaped Solid Propellants through a Combination of Genetic Algorithm and Machine Learning

This study was focused on the configuration design of a star grain by using machine learning in the optimal design process. The key to optimizing the grain design is aimed at obtaining a set of configuration variables that satisfy the requirements. The optimization problem consists of an objective area profile subject to certain constraints and an objective function that quantitatively calculates the design level. Designers must formulate suitable optimization problems to achieve an optimal design. However, because a method to alleviate the influence of the sliver section is not yet available, the optimization problem is typically solved based on experience, which is time- and effort-intensive. Consequently, a more practical and objective grain design method must be developed. In this study, an optimal design method using machine learning was developed to increase the convenience and success rate. A support vector machine was used to train a classification model that predicts a class. The classification model was used to alleviate the influence of the sliver zone and correct the search problem to ensure that an optimal solution existed in the region satisfying the requirements. The proposed method was validated through star grain optimal design using the genetic algorithm. The optimization was performed considering the area profiles, and the effectiveness of the proposed method was demonstrated by the enhanced accuracy.

Autonomous modification and optimization method for rim-driven system in surface ships

This paper proposes the modification of the propulsion system of the surface ship ONRT and the optimization strategy for the stern propulsion system of the modified ship. Specifically, this paper proposes a blade design scheme of a semi-elliptical projection profile with zero skew, zero rake, and equal pitch. An in-house code for autonomous three-dimensional reconstruction is employed to recreate the thruster. Next, a presentation of the ship's propeller optimization design process based on the CAD, CFD, DOE, and Optimization frameworks ensues. In this approach, four surrogate models are applied. The study also performs optimization research and examines the optimization approach for updated designs. The results demonstrate that the Kriging surrogate model provides more accurate and reliable predictions. Compared with the original model, the total resistance of the optimized modified design is reduced by 23.99 %, and the power of the single thruster is reduced by 3.35 %. In addition, the volume occupied by the modified design is only 44% that of the original model. The results of this paper provide effective technical support for the optimization and upgrading of the shaftless rim-driven system of surface ships.

Surrogate-based optimization design for surface texture of helical pair in helical hydraulic rotary actuator

A good surface texture design can effectively improve the tribological performance of the helical pair within a helical hydraulic rotary actuator(HHRA). However, the optimization design process can be time-consuming due to the multiple design variables involved and the complexity of the mathematical model. This paper proposes a modified efficient global optimization (MEGO) method for solving such demanding surface texture design challenges. The MEGO utilizes a Kriging model with the optimized Latin hypercube sampling (OLHS) for initial sampling and the proposed modified expected improvement (MEI) function for sequential sampling. A comparative study of several global optimization algorithms with the MEGO on the surface texture design is performed. Subsequently, surrogate-based optimization and parameter analysis are carried out, resulting in the identification of an optimal set of texture parameters. The findings reveal the superiority of the MEGO in both model prediction accuracy and refinement of minima. Moreover, compared to the base design, the friction coefficient can be reduced by up to 45.2%.

Robust multi-objective building design optimization approach using NSGA-II algorithm considering weather and occupant uncertainties

A novel methodology is presented to evaluate how real-world operational uncertainties impact multi-objective building design optimization process. NSGA-II algorithm is applied across nine distinct operational scenarios, considering diverse weather datasets and energy usage patterns to optimize energy efficiency and thermal comfort in a residential building within India's hot and dry climate. The analysis considers 382,400 possible design options for each scenario. Using (10.5–12.5) % of model evaluations, NSGA-II significantly reduces Annual Cooling Energy Demand (ACED; 46.7%–58.7%) and Cooling Set-point Unmet Hours (CSUH; 70.92%–86.93%) to identify nine Pareto–Optimal solution sets. The final robust optimal design is confirmed by selecting the most repeated parameter settings, representing the highest robustness. Low thermal transmittance walls, double-glazed windows with a 10% window-to-wall ratio (WWR), deep overhangs, and a low solar heat gain coefficient are recognized as robust optimal building components. However, Air Conditioner (AC) sizing lacks robustness in minimizing energy demand across scenarios, requiring case-specific evaluation due to its sensitivity to weather and operational variations. Further, PAWN sensitivity analysis highlighted the substantial impact of WWR (KSWWR ∼ (0.46–0.66)) and wall type (KSWall ∼ (0.31–0.46)) on ACED and CSUH in chosen climate. AC operation is more important for predicting CSUH (KSAC ∼ 0.35) than ACED (KSAC ∼ 0.10).

High purity galacto-oligosaccharides: Optimal process design and prebiotic effect

Prebiotic unpurified commercial galacto-oligosaccharides (GOS) often contain significant amounts of glucose, galactose and lactose. Current purification processes remove these digestible components, especially lactose with a high risk of affecting functionality.In the current study a high purity GOS (HP-GOS) was prepared by treating GOS with β-galactosidase to hydrolyze the remaining lactose into glucose and galactose. Subsequently mono sugars were removed by sequential simulated moving bed (SSMB) chromatography. To this end, a design of experiments (DOE) approach was applied to determine the dry matter content and β-galactosidase concentration that optimally support lactose hydrolysis in such a way that the detrimental effects on GOS composition are minimized to preserve its original oligosaccharide composition. The HP-GOS product obtained using the identified optimal settings for lactose hydrolysis contained more than 75% of the original DP2 GOS fraction while the fraction with a DP above 2 was unaffected by the purification. Using fecal fermentations, it was demonstrated that the GOS parts of the HP-GOS and the parent GOS have comparable effects on gut microbiota composition. Both GOS products equally increased the relative abundances of Bifidobacterium. Furthermore, there were no differences in alpha diversity and in the distribution of the different bifidobacterial species. Based on these outcomes it was concluded that the lactose hydrolysis step of the purification process did not influence gut microbiota modulating effects of GOS.

Dynamic Simulation of Nitrifying Microbial Communities for Establishing Acidic Partial Nitritation in Suspended Activated Sludge.

Acidic partial nitritation (PN) is a promising technology to achieve low-cost and energy-efficient shortcut nitrogen removal from wastewater. However, a comprehensive understanding of the acidic PN under dynamic changes of pH in a sequencing batch reactor (SBR) is still lacking. In this study, we successfully established acidic PN (NO2- accumulation ratio >80%) under dynamic pH variation from 7.0 to 4.5 in a lab-scale SBR. By accumulating in situ free nitrous acid (FNA) generation based on the dynamic pH change, acidic PN maintained stability even at a low NH4+ concentration of 100 mg N L-1. The microbial community analysis revealed that two ammonium-oxidizing bacteria (AOB) genera, Nitrosospira and Nitrosomonas, successfully coexisted and cooperated during acidic PN. None of the species of nitrite-oxidizing bacteria (NOB) showed adaptation to intermittent inhibition of in situ FNA even under high DO conditions (>4.0 mg O2 L-1). Furthermore, we innovatively incorporated the classic nitrification model with the growth and decay of different nitrifying bacterial species and their inhibition by pH, FNA, and free ammonia (FA) to predict the nitrifying microbial communities shifting for establishing acidic PN. The extended model was calibrated by using short-term batch experiments and was validated by using long-term dynamic data of the nitrifying microbial community during SBR operation. The validated model was further used to identify feasible influent conditions for the SBR PN process, including influent HCO3- concentration, NH4+ concentration and molar ratio (HCO3/NH4+). Outcomes from this study support the optimal design of acidic PN-based short-cut nitrogen removal processes for future application.

Two-Step Optimum Design Process of PMSM to Improve Driving Efficiency and Harmonics of Lightweight Electric Vehicle

Two-Step Optimum Design Process of PMSM to Improve Driving Efficiency and Harmonics of Lightweight Electric Vehicle

Optimal design and multicriteria comparison of extractive distillation processes for separating of a quaternary low-boiling solvent mixture

This article mainly explores the eco-efficient separation of a complex quaternary azeotropic mixture of methanol, n-hexane, ethyl acetate and ethanol from a pharmaceutical enterprise via extractive distillation. To this end, aniline (AN) and ethylene glycol (EG) are selected as two alternative entrainers based on the relative volatility curve. Two conventional processes with AN and EG (AN-CED and EG-CED) are proposed and optimized with total annual costs (TAC) as a goal by simulated annealing algorithm. To improve energetic and economic performance, two optimal strengthening processes, that is, low-pressure side-stream extractive distillation process with AN (AN-SSED) and dividing-wall extractive distillation process with EG (EG-EDWC) are also developed for the first time. Finally, the four processes are compared in terms of economics, CO2 emissions and inherent safety. The results show that the AN-SSED process is recommended due to the best economic, environmental and inherent safety properties, as reflected by the lowest TAC, CO2 emission and process route index (PRI). It can reduce TAC by 9.38%, CO2 emission by 26.34% and PRI by 38.02% relative to the EG-EDWC process. Remarkably, the EG-EDWC process can reduce TAC by 11.12% than AN-CED process and 6.9% than the EG-CED process, but it is inferior to the two conventional processes in terms of CO2 emissions and inherent safety.

Automated Control Systems in Greenhouses

Objective. The purpose of the research is to develop an innovative automated program associated with a completely new irrigation regime based on a closed water supply system.Method. Methods of modeling, formalized description, optimal design and control of technological processes and production were used.Result. An automated control system in greenhouse farming based on Arduino controllers and ONI PLR Studio software for its operation are presented. The scientific novelty of the work lies in the creation of appropriate modes of the climate control system in greenhouses. The ONI PLR Studio program is written in FBD language. It solves important problems of controlling the cultivation of agricultural products, breeding new plant varieties, and controlling the technological process using certain signals. User input is limited to software adjustments and direct setting of controller parameters. The adjustment can be carried out remotely through a computer connected to the controller. The automatic operating mode is provided by sensors and a controller with electronic control circuits, which ensure that the actuators operate in the required mode.Conclusion. An automated control system (ACS) allows you to control air and soil temperature, soil and air humidity, soil nutrition, disinfection, air pressure, lighting, the presence of carbon dioxide in the greenhouse, and climate control.

An Improved Two-Dimensional Simplification Calculation Method for Axial Flux Permanent Magnet Synchronous Motor

The axial flux permanent magnet synchronous motor (APMSM) has the advantages of short axial size, high efficiency, and high power density. However, the three-dimensional magnetic circuit structure of this type of motor results in a longer calculation time, which is not conducive to rapid design and optimization. In order to quickly and accurately complete the preliminary calculation and optimization of axial flux permanent magnet synchronous motors, this paper proposes an improved equivalent calculation method for two-dimensional multi-layer linear motors. This method is based on the traditional two-dimensional multi-layer linear motor equivalent method, taking into account the complexity of geometric model establishment and simulation condition settings and applying the principle of analogy to achieve significant simplification of geometric modeling and simulation settings. The article elaborates on the correlation and differences between the proposed method and existing methods and verifies the calculation accuracy through finite element calculation. The results indicate that the improved two-dimensional multi-layer linear motor equivalent calculation method proposed in this article can significantly reduce calculation time while ensuring calculation accuracy and has good application prospects in preliminary design and parameter optimization processes.

Influence of competitive adsorption, diffusion, and dispersion of CH4 and CO2 gases during the CO2-ECBM process

The displacement mechanisms of adsorbed CH4 by injected CO2 at high pressures in CO2-enhanced coalbed methane (CO2-ECBM) processes involve competitive adsorption, diffusion, and dispersion. Inherent coal heterogeneity and the complexity of the CO2-CH4 displacement model make CO2-ECBM modelling and optimal process design challenging. The pure gas isotherms and Langmuir adsorptions constants for CO2 and CH4 were determined using the manometric method. Notably, CO2 shows higher adsorption capacity than methane, and Langmuir volume (VL) for CO2 is 2 to 2.3 times that of methane. High pressure adsorption isotherms and experimentally determined diffusion coefficients set the sorption kinetics. The diffusion coefficient of CH4 for all four samples was in the range between 1.35×10-12 and 7.42×10-12 m2/s, while for CO2, it varied from 10.9×10-12 to 15.48×10-12 m2/s. The new concept of caucus time was introduced in the sorption kinetics of CO2 and CH4. Each sample has distinct caucus time and was positively correlated with Langmuir volumes of CO2 and CH4 with R2 > 0.9. The critical desorption pressure above which CH4 exhibits retrograde adsorption behaviour is evaluated from co-adsorption isotherms adopting Ideal Adsorption Solution Theory (IAST). The critical desorption pressure for the four samples were 1220, 460, 575 and 740 psi respectively. IAST also predicts a critical mole fraction (∼30 %) in the gas phase above which CO2 becomes the dominant adsorbed phase on coal surfaces. The results of Advective-Dispersion modelling at a larger scale illustrate the value of the Dispersion coefficient in forecasting and screening. Since the time available for diffusion versus dispersion depends upon flowrate, high values of dispersion coefficient can negatively influence favourable competitive adsorption expectations through higher operational cost in CO2 separation. This work suggests the existence of both an optimal operational pressure and flowrate to maximize the potential of CO2-ECBM technology demonstrated in competitive adsorption modelling.