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Techno-economics of high ash coal gasification: A machine learning approach using catboost model

JOURNAL OF CLEANER PRODUCTION 2024年 481卷

作者： Singh, Dharmendra Kumar Kumar, Sandeep Talaipalli Coal Min Project Lailunga Rd Raigarh 496111 CG India NTPC Sch Business Plot 5-14Sect 16 A Noida 201301 India

This work explores the techno-economics of high ash coal gasification using circulating fluidized bed (CFB) gasifier technology at the Talaipalli Coal Mining Project, India being developed by NTPC Limited. The research employs the categorical boosting machine learning algorithm to predict syngas composition, yield, and lower heating value offering an innovative approach to optimize the gasification process. The work compares CFB gasification with conventional coal combustion and establishes it as the most suitable gasification technology due to its feedstock flexibility, technological maturity, and cost-effectiveness. A comprehensive financial analysis reveals a favorable net present value (NPV) of Indian Rupee 20150 Million, an internal rate of return (IRR) of 9.54%, and a payback period of 8.3 years for a 65 MWth CFB gasifier. Sensitivity analysis highlights the influence of key factors such as syngas yield, capital costs, and selling price on financial viability. The environmental benefits, including reduced emissions and waste management, further underscore the value of coal gasification for sustainable energy production. This work aligns with government initiatives like the National Coal Gasification Mission, advocating future research in co-gasification and techno-economic assessments for similar projects.

关键词： catboost algorithm Techno-economic analysis High ash coal gasification Circulating fluidized bed technology Syngas yield and financial modelling

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Electric Vehicle Battery State of Charge Estimation Using Metaheuristic-Optimized catboost algorithms

Franklin Open

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Franklin Open 2025年

作者： Mohd Herwan Sulaiman Zuriani Mustaffa Ahmad Salihin Samsudin Amir Izzani Mohamed Mohd Mawardi Saari Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) 26600 Pekan Pahang Malaysia Center for Advanced Industrial Technology (AIT) Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) 26600 Pekan Pahang Malaysia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) 26600 Pekan Pahang Malaysia Ionic Materials Team Faculty of Industrial Sciences & Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) 26300 Gambang Pahang Malaysia

State of Charge (SoC) estimation plays a crucial role in battery management systems for electric vehicles, directly impacting their operational efficiency and reliability. This study presents a hybrid approach combining the catboost algorithm with metaheuristic optimization techniques to enhance SoC estimation accuracy and robustness. The methodology was validated using an extensive dataset collected from 72 real-world driving trips of a BMW i3 (60 Ah), comprising 1,053,910 instances of battery and vehicle operation metrics. A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic algorithm (GA), and Whale Optimization algorithm (WOA), each integrated with catboost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. Experimental results demonstrate that the BMO-catboost approach achieved superior performance with best-case metrics of RMSE = 6.1031, MAE = 4.1303, and R² = 0.8211, outperforming both PSO-catboost, GA-catboost, and WOA-catboost implementations. The framework's effectiveness was validated through rigorous testing, establishing its potential for real-world electric vehicle applications. This research contributes to the advancement of battery management technology, offering promising implications for electric vehicle energy management and broader energy storage applications.

关键词： Battery State of Charge catboost algorithm Machine Learning Metaheuristic algorithms

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Renewable power source energy consumption by hybrid machine learning model

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ALEXANDRIA ENGINEERING JOURNAL 2022年第12期61卷 9447-9455页

作者： Abd El-Aziz, Rasha M. Jouf Univ Coll Sci & Arts Qurayyat Dept Comp Sci Sakakah Saudi Arabia

Today, electricity is in high demand in a variety of places, including hospitals, industry, households, transportation, and communication, among others. Renewable energy is a revolutionary type of energy that is increasingly being used to replace electricity demand because it has been regenerated and reused several times. Renewable energy is an intermediate and unpredictable natural resource, so it is difficult for many research studies to estimate its rate. To address this problem, this study uses a hybrid machine learning technique to precisely predict the energy level of natural resources. The hybrid machine learning is the combination of Multilayer Perceptron (MLP), Support Vector Regression (SVR) and catboost algorithm that increases the performance and predictability of renewable energy consumption. The proposed system dataset's results are evaluated at the train and test levels, and the results are then compared to other current approaches. The end results reveal that the proposed hybrid machine learning technique has a high prediction level when compared to others, as well as a lower cost rate and improved overall system performance.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Machine Learning Renewable Power Source Multilayer Perception catboost algorithm

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A novel feature engineered-catboost-based supervised machine learning framework for electricity theft detection

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ENERGY REPORTS 2021年 7卷 4425-4436页

作者： Hussain, Saddam Mustafa, Mohd Wazir Jumani, Touqeer A. Baloch, Shadi Khan Alotaibi, Hammad Khan, Ilyas Khan, Afrasyab Univ Teknol Malaysia Sch Elect Engn Johor Baharu 81310 Malaysia Mehran Univ Engn & Technol Dept Elect Engn SZAB Campus Khairpur Mirs 66020 Pakistan Mehran Univ Engn & Technol Jamshoro Dept Mechatron Engn Sindh 76062 Pakistan Taif Univ Coll Sci Dept Math POB 11099 At Taif 21944 Saudi Arabia Majmaah Univ Coll Sci Al Zulfi Dept Math Al Majmaah 11952 Saudi Arabia South Ural State Univ Inst Engn & Technol Dept Hydraul & Hydraul & Pneumat Syst Lenin Prospect 76 Chelyabinsk 454080 Russia

This paper presents a novel supervised machine learning-based electric theft detection approach using the feature engineered-catboost algorithm in conjunction with the SMOTETomek algorithm. Contrary to the previous literature, where the missing observations in data are either ignored or imputed with average values, this work utilizes k-Nearest neighbor technique for missing data imputation;thus, an accurate and realistic estimation of the missing data is achieved. To mitigate the biasness to the majority data class, the proposed model utilizes the SMOTETomek algorithm, which neutralizes the mentioned effect by managing a proper balance between over-sampling and under-sampling techniques. Feature Extraction and Scalable Hypothesis (FRESH) algorithm is utilized at the later stage of the proposed NTL detection framework to extract and select the most relevant data features from the provided dataset. Afterward, the model is trained using the catboost algorithm to classify the consumers into two distinct categories, i.e., genuine and theft. Finally, to interpret the model's decision for the corresponding predictions, the tree-SHAP algorithm is utilized. To validate the efficacy of the proposed ML based theft detection approach, its performance is compared with that of the traditional gradient boosting ML algorithms such as XGBoost, lightGBM, Ensemble bagging, boosting ML models, and other conventional ML models using five of the most widely used performance metrics, i.e., precision, accuracy, F1score Kappa and MCC. The proposed technique achieved an accuracy of 93% and a detection rate of 92%, which is significantly higher than all the considered competing algorithms under identical dataset and hyperparameters. (C) 2021 Published by Elsevier Ltd.

关键词： catboost algorithm NTL detection Smart meters Feature engineering Machine learning model interpretation

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Research on Wind Turbine Fault Detection Based on the Fusion of ASL-catboost and TtRSA

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SENSORS 2023年第15期23卷 6741-6741页

作者： Kong, Lingchao Liang, Hongtao Liu, Guozhu Liu, Shuo Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-catboost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-catboost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-catboost algorithm, the TtRSA-ASL-catboost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-catboost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

关键词： wind turbine catboost algorithm fault detection category imbalance intelligent optimization algorithm

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Multi-functional optimization of mechanical strength and electrical properties in graphite-reinforced cement composites using a hybrid catboost-NSGA-II framework

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MATERIALS TODAY COMMUNICATIONS 2025年 46卷

作者： Abbas, Yassir M. Alsaif, Abdulaziz King Saud Univ Coll Engn Dept Civil Engn Riyadh 12372 Saudi Arabia

The trade-off of mechanical strength and electrical conductivity in cement composites poses a persistent challenge due to the conflicting nature of these properties. Addressing this issue, the present study introduces a novel multi-objective optimization framework specifically designed for graphite nanomaterial-reinforced cement composites. This advanced framework combines the predictive power of the categorical boosting (catboost) algorithm with the optimization capabilities of the non-dominated sorting genetic algorithm (NSGA-II) to carry out the simultaneous enhancement of compressive strength (CS) and electrical resistivity (ER). The research is driven by comprehensively curated databases, which are further refined using cutting-edge methods to maximize data reliability and relevance. Among the machine learning models evaluated, catboost emerged as the most accurate delivers predictions for CS (R2 = 96.5 %, MAE = 1.6 MPa) and ER (R2 = 99.4 %, MAE = 2.26 kS2 center dot cm). Critical insights from the validated ML models reveal that curing age, water-to-cement ratio, and thickness of graphite particles are pivotal factors influencing CS, while volumetric graphite content plays a dominant role in establishing conductive pathways that lower ER. Through the NSGA-II framework, optimized formulations achieved remarkable CS of up to 97.2 MPa and minimized electrical resistivities to 0.6 kS2 center dot cm. Moreover, prolonged ultrasonication times and elevated graphite dosages were shown to enhance nanomaterial dispersion reduce particle agglomeration, and fortify the conductive networks in the cement matrix.

关键词： catboost algorithm Electrically conductive cementitious composites Graphite nanomaterials Multi-objective optimization NSGA-II framework

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Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

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WATER 2024年第19期16卷 2786页

作者： Liu, Weitao Han, Mengke Zhao, Jiyuan Shandong Univ Sci & Technol Coll Safety & Environm Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7 similar to 414.9 m and 750 similar to 900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 > D1 > D3 > D4, while, in the other two, it was D1 > D2 > D3 > D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified

关键词： floor failure depth K-means clustering algorithm grey correlation analysis catboost algorithm groundwater

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A machine learning model of liquid bridge force and its application in discrete element method

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CONSTRUCTION AND BUILDING MATERIALS 2024年 411卷

作者： Cui, Hanwen Zhao, Honghua Ji, Shunying Zhang, Xingli Awadalseed, Waleed Tang, Hongxiang Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116023 Peoples R China Dalian Univ Technol Dept Engn Mech Dalian 116023 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116023 Peoples R China

Developing an accurate and efficient contact model for discrete element method (DEM) simulation that can describe the microscopic dynamics of low moisture content wet granular systems continues to be a major challenge. In this study, a improved energetic approach based on the traditional linear unit discretization method is introduced to solve liquid bridge problems. This improved approach significantly ameliorates the efficiency of constructing the databases while ensuring high data accuracy. Using this method, we generated databases consisting of 163,440,000 and 2043,000 data points for the sphere-sphere and sphere-wall systems, respectively. Machine learning capillary force models (ML-Capillary) are trained using the catboost algorithm. These models demonstrate excellent performance in predicting capillary force between particles as well as between particles and walls, considering different particle sizes, moisture levels, and contact angles, while neglecting the influence of gravity. The corresponding relative errors are below 1.5% and 2%, respectively, which are lower than existing simulation models. We incorporated the obtained models into the discrete element method and calculated capillary force between particle pairs and simulated wet granular column collapse tests. The simulation results show good agreement with experimental results in terms of parameters such as inter-particle capillary forces and particle column collapse run-out length. This study will provide an accurate and efficient numerical model for simulating low saturation wet granular systems using the discrete element method.

关键词： Discrete element method Liquid bridge Machine learning catboost algorithm Geometric approximation energetic approach

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A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2021年第2期56卷 279-302页

作者： Jain, Nishant Tomar, Abhinav Jana, Prasanta K. Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India

Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The catboost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the catboost algorithm outperforms other ML algorithms.

关键词： Employee churn Employee importance model Retention policy catboost algorithm MADM method TOPSIS

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Classification of Liver Disease Using Conventional Tree-Based Machine Learning Approaches with Feature Prioritization Using a Heuristic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第11期15卷 357-363页

作者： Mondal, Proloy Kumar Byeon, Haewon Khulna Univ Engn & Technol Dept Elect & Commun Engn Khulna Bangladesh Inje Univ Dept Digital Antiaging Healthcare BK21 Gimhae 50834 South Korea

Liver disease ranks as one of the leading causes of mortality globally, often going undetected until advanced stages. This study aims to enhance early detection of liver disease by employing machine learning models that utilize key health indicators. Utilizing the Indian Liver Patient Dataset (ILPD) from the UCI repository, we developed a predictive model using the catboost algorithm, achieving an initial accuracy of 74%. To improve this, feature selection was performed using the Whale Optimization algorithm (WOA) and Harris Hawk Optimization (HHO), which increased accuracy to 82% and 85% respectively. The methodology involved preprocessing to correct data imbalances and outlier removal through univariate and bivariate analyses. These optimizations highlight the critical features enhancing the model's predictive capability. The results indicate that integrating metaheuristic algorithms in feature selection significantly improves the accuracy of liver disease prediction models. Future research could explore the integration of additional datasets and machine learning models to further refine predictive capabilities and understand the underlying pathophysiology of liver diseases.

关键词： Liver disease classification prediction catboost algorithm machine learning optimization algorithm

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