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Gasification process modelling and optimization using Gaussian process regression and hybrid population-based algorithms

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 4151-4171页

作者： Si, Hongying Shangqiu Normal Univ Sch Math & Stat Shangqiu 476000 Henan Peoples R China

Gasification holds a central role in the thermochemical conversion of diverse carbon-rich feedstocks into valuable syngas, making a substantial contribution to the advancement of environmentally sustainable clean energy generation. The methodical modelling and optimization of gasification processes hold paramount significance in augmenting their overall operational efficiency. As part of the ongoing research endeavour, a novel approach is introduced that combines Gaussian Process Regression (GPR) modelling with the population-based vortex search algorithm (PVSA) and the Dingo Optimization algorithm (DOA). The core aim of this methodology is to enhance and optimize gasification processes. GPR serves as a surrogate model used to proficiently capture the intricate relationships between input variables and gasification performance metrics. The implementation of GPR ensures predictive accuracy, facilitating a more streamlined exploration of the design space while concurrently reducing the demands on computational resources. The integration of GPR modelling in conjunction with the hybrid approach, incorporating PVSA and DOA, markedly augments both the efficiency and precision in the design and control of gasification processes. The GPPV hybrid model has achieved the most optimal result with the highest R2 value of 0.989 and 0.987 for the CH4 and C2Hn and the lowest RMSE of 0.476 and 0.164 for CH4 and C2Hn, indicating the reliability of the PVSA in optimizing the GPR model in predicting the syngas of gasification process. The framework expounded upon in this investigation provides a sturdy foundation for the progression of gasification technology, encompassing a diverse array of applications in the domains of clean energy production and sustainability endeavours.

关键词： Syngas Gasification Gaussian process regression population-based vortex search algorithm Dingo optimization algorithm

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Prediction of pile settlement by using hybrid random forest models

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2087-2101页

作者： Yu, Shengnan Henan Tech Coll Construct Dept Architecture Zhengzhou 450064 Henan Peoples R China

Pile settlement, a critical aspect of geotechnical engineering, refers to the vertical movement or deformation of piles, which are structural elements used to support buildings, bridges, and other infrastructure. Accurate prediction of pile settlement is essential for ensuring the stability and safety of these structures. The need for novel techniques in pile settlement prediction arises from the complexity of geotechnical systems and the desire for higher precision and efficiency in the assessment. Traditional methods often have limitations in handling the intricate interactions between soil properties and pile behavior. This necessitates the exploration of advanced approaches, such as machine learning and optimization algorithms, to enhance prediction accuracy. In this study, the performance of a Random forest (RF) model, a machine learning technique, was evaluated for forecasting mound payment. To further improve accuracy of model's and optimization, three metaheuristic optimizers were utilized: population-based vortex search algorithm (PVS), Atom search Optimization (ASO), and Golden Sine algorithm (GSA). Notably, the GSA-optimized RF model exhibited superior performance, achieving the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values. Furthermore, a comparative analysis of the three optimizers revealed that GSA outperformed ASO and PVS in terms of convergence rate and solution quality. This integration of machine learning and optimization techniques represents an innovative approach that significantly enhances the precision of pile settlement prediction. It highlights the potential for these tools to be applied more broadly in geotechnical engineering. Future studies can further explore the application of this approach to diverse datasets and assess its effectiveness across various soil conditions, offering promising avenues for further advancements in the field.

关键词： Pile settlement Random forest population-based vortex search algorithm Atom search optimization Golden sine algorithm

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Having Deep Investigation on Predicting Unconfined Compressive Strength by Decision Tree in Hybrid and Individual Approaches

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 127-139页

作者： Zhang, Qingqing Wang, Lei Gu, Hongmei Hebei Inst Commun Informat Technol & Cultural Management Inst Shijiazhuang 051430 Hebei Peoples R China

In the field of geotechnical engineering Rocks' unconfined compressive strength (UCS) is an important variable that plays a significant part in civil engineering projects like foundation design, mining, and tunneling. These projects' stability and safety depend on how accurately UCS predicts the future. In this study, machine learning (ML) techniques are applied to forecast UCS for soil-stabilizer combinations. This study aims to build complex and highly accurate predictive models using the robust Decision Tree (DT) as a primary ML tool. These models show relationships between UCS considering a variety of intrinsic soil properties, including dispersion, plasticity, linear particle size shrinkage, and the kind of and number of stabilizing additives. Furthermore, this paper integrates two meta-heuristic algorithms: the population-based vortex search algorithm (PVS) and the Arithmetic optimizer algorithm (AOA) to enhance the precision of models. These algorithms work in tandem to bolster the accuracy of predictive models. This study has subjected models to rigorous validation by analyzing UCS samples from different soil types, drawing from historical stabilization test results. This study unveils three noteworthy models: DTAO, DTPB, and an independent DT model. Each model provides invaluable insights that support the meticulous projection of UCS for soil-stabilizer blends. Notably, the DTAO model stands out with exceptional performance metrics. With an R-2 value of 0.998 and an impressively low RMSE of 1.242, it showcases precision and reliability. These findings not only underscore the accuracy of the DTAO model but also emphasize its effectiveness in predicting soil stabilization outcomes.

关键词： Unconfined compressive strength machine learning decision tree population-based vortex search algorithm arithmetic optimizer algorithm

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Forecasting Residential Building Heating Load With An Innovative Gaussian Process Regression Method

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第6期28卷 1219-1231页

作者： Sun, Xiaoyu Dalian Neusoft Univ Informat Sch Digital Arts & Design Dalian 116023 Liaoning Peoples R China

Effectively controlling the heating load (HL) in residential buildings is a vital component of energy conservation and sustainability. This abstract presents a new methodology for predicting HL by incorporating Gaussian Process Regression (GPR) and harnessing the power of two groundbreaking optimization techniques: the population-based vortex search algorithm (PVS) and the Flow Direction algorithms (FDA). GPR stands out as a robust machine learning algorithm renowned for its capacity to grasp intricate data relationships. Combining these mentioned optimizers with the GPR model results in a hybrid strategy that harnesses the unique advantages of each element. PVS and FDA are utilized to optimize the GPR's parameters, thereby elevating its predictive precision. The amalgamation of GPR, PVS, and FDA surpasses conventional techniques and even standalone GPR models regarding predictive precision and convergence velocity. This methodology offers a pragmatic and efficient approach to enhancing the forecast of HL in residential buildings, consequently aiding in better energy management and mitigating environmental impact. The hybrid GPPV model distinguishes itself with its exceptional accuracy when compared to alternative proposed models. Boasting a low RMSE of 1.013 and a R2 value of 0.990, GPPV attains the highest performance level. Furthermore, this research paves the way for the exploration of employing nature-inspired optimization techniques alongside neural networks to address a wide array of intricate challenges. The combined influence of GPR and these inventive optimizers highlights the capacity of hybrid models to tackle practical, real-world issues.

关键词： Heating load Gaussian Process Regression population-based vortex search algorithm Flow Direction algorithms

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Applying Support Vector Regression-based Hybrid Models For Modeling The Gasification Process

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第6期28卷 1195-1208页

作者： Gong, Baofu Criminal Invest Police Univ China Lib Shenyang 110854 Peoples R China

Modeling the gasification process through machine learning (ML) involves predicting the behavior and performance of gasification systems. Support Vector Regression (SVR) is known as an effective procedure in forecasting continuous variables and is even suitable for the modeling gasification process. Employing optimization algorithms to connect and fine-tune internal model settings can lead to the creation of various hybrid and ensemble models. Generally, employing hybrid models has demonstrated enhanced performance while utilizing cost-effective modeling techniques. In this study, SVR was utilized as a machine learning method, alongside the Crystal Structure algorithm (CryStAl) and the population-based vortex search algorithm (PVSA), to fine-tune SVR for accurately assessing CO and CO2 values. After evaluating the outcomes of the proposed models, it was observed that the SVR-PVSA hybrid model outperformed the SVR-CryStAl model, with differences of 1%, 19%, and 57% based on R2, RMSE, and MAE indices, respectively for CO and that of 1%, 14%, and 54% for CO2 in terms of R2, RMSE, and MAE evaluators, respectively. Furthermore, for predicting both CO and CO2, the SVR-CryStAl hybrid model yielded the highest value, demonstrating superior performance compared to the SVR-PVSA model, with an average difference of 0.6% and 0.9% in terms of the VAF index.

关键词： Gasification modeling Machine learning Support Vector Regression Crystal Structure algorithm population-based vortex search algorithm

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Predictive Modeling of Student Performance Through Classification with Gaussian Process Models

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第6期15卷 1214-1227页

作者： Zhang, Xiaowei Yue, Junlin Luoyang Inst Sci & Technol Dept Educ Sci & Mus Luoyang 471023 Peoples R China Luoyang Normal Univ Dept Foreign Language Teaching & Res Luoyang 471934 Peoples R China

In the contemporary educational landscape, proactively engaging in predictive assessment has become indispensable for academic institutions. This strategic imperative involves evaluating students based on their innate aptitude, preparing them adequately for impending examinations, and fostering both academic and personal development. Alarming statistics underscore a notable failure rate among students, particularly in courses. This article aims to employ predictive methodologies to assess and anticipate the academic performance of students in language courses during the G2 and G3 academic exams. The study utilizes the Gaussian Process Classification (GPC) model in conjunction with two optimization algorithms, the population-based vortex search algorithm (PVS) and the COOT Optimization algorithm (COA), resulting in the creation of GPPV and GPCO models. The classification of students into distinct performance categories based on their language scores reveals that the GPPV model exhibits the highest concordance between measured and predicted outcomes. In G2, the GPPV model demonstrated the notable 51.1% correct categorization of students as Poor, followed by 25.57% in the Acceptable category, 14.17% in the good category, and 7.7% in the Excellent category. This performance surpasses both the optimized GPCO model and the singular GPC model, signifying its efficacy in predictive analysis and educational advancement.

关键词： Academic performance language hybrid algorithms Gaussian Process Classification population-based vortex search algorithm COOT Optimization algorithm

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Utilizing meta-heuristic algorithms for load-bearing capacity prediction in piles with support vector regression

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第6期7卷 5445-5459页

作者： Xu, Mengran Zhu, Zhihui Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310000 Zhejiang Peoples R China Zhejiang Construct Investment Transportat Infrastr Engn Co 4 Hangzhou 310000 Zhejiang Peoples R China Guangzhou Maritime Univ Guangzhou 510725 Guangdong Peoples R China

When designing foundations and using geotechnical engineering, pile bearing capacity (Pu) is essential, indicating the maximum load piles can sustain without failure. Accurate Pu calculation ensures structural stability and safety, considering soil conditions and structural loads. Methods range from empirical equations to advanced numerical analyses, factoring in soil characteristics and pile dimensions. Recent innovations like machine learning enhance prediction accuracy. Understanding Pu is vital for optimizing pile designs, reducing risks, and promoting resilient, sustainable construction. Advances in Pu prediction promise continued improvements in construction practices. This research employs the support vector regression (SVR) model as a key problem-solving approach in developing a robust machine-learning framework. To improve the accuracy and performance of the model, it integrates two distinct meta-heuristic optimization techniques: the population-based vortex search algorithm (PVSA) and the Electric Charged Particles Optimization (ECPO). These optimization methods are strategically harnessed to fine-tune the SVR model parameters, ensuring the attainment of optimal outcomes. By leveraging ECPO and PVSA, the research aims to push the boundaries of predictive accuracy and computational efficiency in machine-learning applications. based on the results obtained, it became apparent that the SVPV (SVR + PVSA) model, which amalgamates the SVR model with the PVSA optimization technique, yielded the most precise estimations for Pu values. This conclusion was supported by an 0.995 R2 and an 31.305 RMSE value, both of which underscored the model's exceptional predictive capabilities.

关键词： population-based vortex search algorithm Machine-learning Support vector regression Electric Charged Particles Optimization Pile bearing capacity

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