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Process design and muti-objective optimization of solid waste/biomass co-gasification considering tar formation
(2024-11-01)
Aentung, Tanawat
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Wu, Wei
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Patcharavorachot, Yaneeporn
Background: The co-gasification of solid waste and biomass to produce syngas is an environmentally friendly technology. Unfortunately, the tar formation in the solid waste/biomass co-gasification process would degrade the product gas quality and the overall process efficiency. Methods: In this study, the kinetics of the solid waste/biomass co-gasification is shown by the Aspen Plus simulation. Through the model validation and sensitivity analysis, it is validated that tar yield, syngas composition, and syngas yield are sensitive to gasifier temperature, steam-to-feed ratio (S/F), and blending weight ratio (B/W). It shows that the increase of the product gas yield (GY) increases CO<inf>2</inf> concentration in the product gas, but the tar yield is reduced. To address the sustainable solid waste/biomass co-gasifier, the multi-objective optimization (MOO) algorithm is implemented to maximize GY and minimize CO<inf>2</inf> concentration. For solving the MOO problem, the standard genetic algorithm (GA) coupled with response surface methodology (RSM) is performed to find the Pareto frontier plot, and the technique for order of preference by similarity to the ideal solution (TOPSIS) is used to determine optimal operating conditions. Significant Findings: Under the Pareto frontier plot and TOPSIS, a GY of 2.672 Nm³/kg, CO<inf>2</inf> concentration of 8.045 vol.%, and tar yield of 17.0617 g/Nm³ can be achieved under the optimal conditions of T = 1099.95 °C, S/F ratio = 0.79, and B/W ratio = 10.02. In addition, the CO<inf>2</inf> absorption using CaO is added to purify CO<inf>2</inf> up to 99.999 % of purity.
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Reliability-Based Design Optimization of a Goland Wing with a Two-Step Approach
(2022-01-01)
Chanu, Suwapat
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Wattanathorn, Alfan
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Senpong, Moses
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Sleesongsom, Suwin
This research proposes to design a Goland wing structure using a two-step approach that concerns the last step uncertainty. The first step starts with performing the design optimization for multi-purposes followed by the design of wing mass, stress, and buckling factor and the reliability test of all solution set. Due to the aircraft wing being subjected to aerodynamics loads, both the structure failure and the material can deviate from the optimum result. A vortex lattice method is used for aerodynamics analysis, the finite element method is analyzed by the structural failure. These techniques are expected to reduce the complexity of Reliability-Based Design Optimization (RBDO). The Latin hypercube sampling method is used to quantify uncertainties of the aircraft wing structural design so that the experimental results including the solution sets are more acceptable and reliable which the proposed approach can be an alternative way for the RBDO technique.
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Multi-objective reliability-based topology optimization of structures using a fuzzy set model
(2020-10-01)
Sleesongsom, Suwin
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Bureerat, Sujin
This research proposes a multi-objective reliability-based topology optimization (MORBTO) for structural design, which considers uncertain structural parameters based on a fuzzy set model. The new technique is established in the form of multi-objective optimization where the equivalent possibilistic safety index (EPSI) is included as one of the objective functions along with mass, and compliance. This technique can reduce complexity due to a double-loop nest problem used previously due to performing single objective optimization. The present technique can accomplish within one optimization run using a multi-objective approach. Two design examples are used to demonstrate the present technique, which have the objectives as structural mass and compliance with the constraint of structural strength. The results show the proposed technique is effective and simple compared to previous techniques.
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Multi-objective, reliability-based design optimization of a steering linkage
(2020-09-01)
Sleesongsom, Suwin
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Bureerat, Sujin
Reliability-based design optimization (RBDO) of a mechanism is normally based on the non-probabilistic model, which is viewed as failure possibility constraints in each optimization loop. It leads to a double-loop nested problem that causes a computationally expensive evaluation. Several methods have been developed to solve the problem, which are expected to increase the realization of optimum results and computational efficiency. The purpose of this paper was to develop a new technique of RBDO that can reduce the complexity of the double-loop nested problem to a single-loop. This involves using a multi-objective evolutionary technique combined with the worst-case scenario and fuzzy sets, known as a multi-objective, reliability-based design optimization (MORBDO). The optimization test problem and a steering linkage design were used to validate the performance of the proposed technique. The proposed technique can reduce the complexity of the design problem, producing results that are more conservative and realizable.
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Multi-objective optimization of a steering linkage using alternative objective functions
(2019-01-01)
Sleesongsom, Suwin
;
Bureerat, Sujin
This paper proposes comparing objective functions in multi-objective optimization of a rack-and-pinion steering linkage in which the optimum results can be affected by the types of objective functions especially when using evolutionary algorithms. The optimization of a steering linkage in the past studied minimizing a steering error and/or a turning radius which can be formulated as a single or multi-objective optimization problem. Steering error usually defines as the different angle between actual angle of steering wheels and the theoretical angle of the wheels according to the Ackerman’s principal. Alternatively, the steering error can be rearranged in form of a deviation of instantaneous center based on the same principal, but it still needs to clarify the advantage of using different steering error measures. As a result, it is our attention to study the effect of objective functions to the optimum results on multi-objective optimization of a rack-and-pinion steering linkage. The objective functions are assigned to simultaneously minimize a steering error (dimensionless angle or length) and a turning radius. The design variables are linkage dimensions. The design problem is solved by improving the hybridization of real-code population-based incremental learning and differential evolution (RPBIL-DE). The comparison shows that the alternative objective function can compare with the traditional objective, which leads to effective design of rack-and-pinion steering linkages.
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Topology optimisation using MPBILs and multi-grid ground element
(2018-02-12)
Sleesongsom, Suwin
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Bureerat, Sujin
This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions.
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Optimization of steering linkage including the effect of McPherson strut front suspension
(2018-01-01)
Sleesongsom, Suwin
;
Bureerat, Sujin
This paper proposes to optimise the steering linkage including an effect of McPherson strut front suspension. Usually, the suspension is exerted with an impact force due to uneven road, which dynamically changes to performance of a steering linkage. The present work proposes to study an effect of suspension to performance of steering mechanism with comparative study of steering mechanism with and without suspension system, which is included in optimization problem. The performance is minimised in both turning radius and steering error, that is called multi-objective optimisation problems. The model of McPherson strut front suspension is simplified model but it is sufficient accuracy. The results show that the suspension is an important effect on the optimisation design and the optimisation results show that the design concept leads to effective design of rack and pinion steering linkages satisfying both steering error and turning radius criteria.
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Multi-objective genetic programming with redundancy-regulations for automatic construction of image feature extractors
(2010-01-01)
Watchareeruetai, Ukrit
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Matsumoto, Tetsuya
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Takeuchi, Yoshinori
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Kudo, Hiroaki
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Ohnishi, Noboru
We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-TT. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-TT and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem. Copyright © 2010 The Institute of Electronics, Information and Communication Engineers.

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