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Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

《环境科学与工程前沿（英文）》 2024年 第18卷 第2期 doi: 10.1007/s11783-024-1777-6

摘要：

● A machine learning approach was applied to predict free chlorine residuals.

关键词： Machine learning     Data-driven modeling     Drinking water treatment     Disinfection     Chlorination    

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Development of machine learning multi-city model for municipal solid waste generation prediction

《环境科学与工程前沿（英文）》 2022年 第16卷 第9期 doi: 10.1007/s11783-022-1551-6

摘要：

● A database of municipal solid waste (MSW) generation in China was established.

关键词： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

《能源前沿（英文）》 2024年 第18卷 第2期   页码 223-240 doi: 10.1007/s11708-023-0891-7

摘要： As the intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificial intelligence (AI) approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation. This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural network (NN) methods in ML for lithium-ion battery SOH simulation and prediction. NN is one of the important branches of ML, in which the application of NNs such as backpropagation NN, convolutional NN, and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention. Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency, low energy consumption, high robustness, and scalable models. In the future, NN can make a greater contribution to lithium-ion battery management by, first, utilizing more field data to play a more practical role in health feature screening and model building, and second, by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent. The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science, reliability, stability, and robustness of lithium-ion battery management.

关键词： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 237-250 doi: 10.1007/s11705-021-2058-6

摘要： Advanced model-based control strategies, e.g., model predictive control, can offer superior control of key process variables for multiple-input multiple-output systems. The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization. This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control. To showcase this approach, five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system. This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges. These controllers also had reasonable per-iteration times of ca. 0.1 s. This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which, in the face of process uncertainties or modelling limitations, allow rapid and stable control over wider operating ranges.

关键词： nonlinear model predictive control     black-box modeling     continuous-time system identification     machine learning     industrial applications of process control    

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Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1097-1109 doi: 10.1007/s11709-020-0634-3

摘要： Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

关键词： compound channel     machine learning     SKM model     shear stress distribution     data mining models    

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Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markovmodel

《工程管理前沿（英文）》 doi: 10.1007/s42524-024-0082-1

摘要： The accurate estimation of geological risks is essential for preventing geohazards, and ensuring efficient and safe construction processes. This study proposes a method, the online hidden Markov model (OHMM), which combines online learning with the hidden Markov model to estimate geological risks. The OHMM is tailored for the continuous nature of observational data, allowing it to adaptively update with each new piece of data. To address the challenge of limited data in the early stages of construction, we use pre-construction borehole samples as additional data. This approach extends the short sequence of observed data to match the length of a complete sequence through an observation extension mechanism. The effectiveness of the OHMM, equipped with this observation extension mechanism, is demonstrated in a case study that models geological risks for a tunnel excavation project in Singapore. The OHMM outperforms traditional methods, including the hidden Markov model, long short-term memory network, neural network, and support vector machine, in predicting geological risks ahead of the tunnel boring machine. Notably, the OHMM can accurately forecast geological risks in areas yet to be constructed, using limited observational and site investigation data. This research advances geological risk prediction models by offering an online updating capability for tunnel excavation and construction projects. It enables early-stage risk prediction and provides long-term forecasts with minimal historical data requirements, maximizing the use of site investigation data.

关键词： geological risk prediction     machine learning     online learning     hidden Markov model     borehole logging    

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Machine learning enabling prediction in mechanical performance of Ti6Al4V fabricated by large-scale laserpowder bed fusion via a stacking model

《机械工程前沿（英文）》 2024年 第19卷 第4期 doi: 10.1007/s11465-024-0796-0

摘要： Determining appropriate process parameters in large-scale laser powder bed fusion (LPBF) additive manufacturing pose formidable challenges that necessitate advanced approaches to minimize trial-and-error during experimentation. This work proposed a data-driven approach based on stacking ensemble learning to predict the mechanical properties of Ti6Al4V alloy fabricated by large-scale LPBF for the first time. This method can adapt to the complexity of large-scale LPBF data distribution and exhibits a more generalized predictive capability compared to base models. Specifically, the stacking model utilized artificial neural network (ANN), gradient boosting regressor, kernel ridge regression, and elastic net as base models, with the Lasso model serving as the meta-model. Bayesian optimization and cross-validation were utilized for model optimization and training based on a limited data set, resulting in higher predictive accuracy compared to traditional artificial neural network model. The statistical analysis of the ANN and stacking models indicates that the stacking model exhibits superior performance on the test set, with a coefficient of determination value of 0.944, mean absolute percentage error of 2.51%, and root mean squared error of 27.64, surpassing that of the ANN model. All statistical metrics demonstrate superiority over those obtained from the ANN model. These results confirm that by integrating the base models, the stacking model exhibits superior predictive stability compared to individual base models alone, thereby providing a reliable assessment approach for predicting the mechanical properties of metal parts fabricated by the LPBF process.

关键词： machine learning     laser powder bed fusion     ensemble learning     stacking algorithm     additive manufacturing    

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Spatial prediction of soil contamination based on machine learning: a review

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1

摘要：

● A review of machine learning (ML) for spatial prediction of soil contamination.

关键词： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Advancing agriculture with machine learning: a new frontier in weed management

《农业科学与工程前沿（英文）》 doi: 10.15302/J-FASE-2024564

摘要：

● Machine learning offers innovative and sustainable weed management approaches.

关键词： Weed management     herbicides     machine learning     agricultural practices     environmental impact    

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 990-1002 doi: 10.1007/s11709-022-0864-7

摘要： This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.

关键词： artificial neural network     grey wolf optimize algorithm     compressive strength     self-compacting concrete    

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Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

《环境科学与工程前沿（英文）》 2023年 第17卷 第11期 doi: 10.1007/s11783-023-1738-5

摘要：

● A novel integrated machine learning method to analyze O3 changes is proposed.

关键词： Ozone     Integrated method     Machine learning    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 183-197 doi: 10.1007/s11705-021-2073-7

摘要： Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations, so that solvability becomes a practical issue. Additional constraints from technical, economic, environmental, and safety considerations may further limit the feasible solution space beyond the convergence requirement. A priori, the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice. To support the exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning models has recently been proposed. However, that approach only considers the exploration of the convergent domain and ignores additional constraints. In this paper, we present an improvement which particularly takes the fulfillment of constraints into account. We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

关键词： machine learning     flowsheet simulations     constraints     exploration    

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Optimization of kinetic mechanism for hydrogen combustion based on machine learning

《化学科学与工程前沿（英文）》 2024年 第18卷 第11期 doi: 10.1007/s11705-024-2487-0

摘要： The reduced mechanism based on the minimized reaction network method can effectively solve the rigidity problem in the numerical calculation of turbulent internal combustion engine. The optimization of dynamic parameters of the reduced mechanism is the key to reproduce the experimental data. In this work, the experimental data of ignition delay times and laminar flame speeds were taken as the optimization objectives based on the machine-learning model constructed by radial basis function interpolation method, and pre-exponential factors and activation energies of H2 combustion mechanism were optimized. Compared with the origin mechanism, the performance of the optimized mechanism was significantly improved. The error of ignition delay times and laminar flame speeds was reduced by 24.3% and 26.8%, respectively, with 25% decrease in total mean error. The optimized mechanism was used to predict the ignition delay times, laminar flame speeds and species concentrations of jet stirred reactor, and the predicted results were in good agreement with experimental results. In addition, the differences of the key reactions of the combustion mechanism under specific working conditions were studied by sensitivity analysis. Therefore, the machine-learning model is a tool with broad application prospects to optimize various combustion mechanisms in a wide range of operating conditions.

关键词： hydrogen combustion     machine learning     chemical kinetics     mechanism optimization    

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Evaluation and prediction of slope stability using machine learning approaches

《结构与土木工程前沿（英文）》 2021年 第15卷 第4期   页码 821-833 doi: 10.1007/s11709-021-0742-8

摘要： In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. By analyzing the evaluation indicators R 2, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.

关键词： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

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Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

《结构与土木工程前沿（英文）》 2024年 第18卷 第3期   页码 444-460 doi: 10.1007/s11709-024-1050-x

摘要： Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete (RC) members in torsion, torsional mechanism exploration and torsional performance prediction have always been difficult. In the present paper, several machine learning models were applied to predict the torsional capacity of RC members. Experimental results of a total of 287 torsional specimens were collected through an overall literature review. Algorithms of extreme gradient boosting machine (XGBM), random forest regression, back propagation artificial neural network and support vector machine, were trained and tested by 10-fold cross-validation method. Predictive performances of proposed machine learning models were evaluated and compared, both with each other and with the calculated results of existing design codes, i.e., GB 50010, ACI 318-19, and Eurocode 2. The results demonstrated that better predictive performance was achieved by machine learning models, whereas GB 50010 slightly overestimated the torsional capacity, and ACI 318-19 and Eurocode 2 underestimated it, especially in the case of ACI 318-19. The XGBM model gave the most favorable predictions with R2 = 0.999, RMSE = 1.386, MAE = 0.86, and λ¯ = 0.976. Moreover, strength of concrete was the most sensitive input parameters affecting the reliability of the predictive model, followed by transverse-to-longitudinal reinforcement ratio and total reinforcement ratio.

关键词： RC members     torsional capacity     machine learning models     design codes    

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标题 作者 时间 类型 操作

Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

期刊论文

Development of machine learning multi-city model for municipal solid waste generation prediction

期刊论文

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

期刊论文

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

期刊论文

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

期刊论文

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markovmodel

期刊论文

Machine learning enabling prediction in mechanical performance of Ti6Al4V fabricated by large-scale laserpowder bed fusion via a stacking model

期刊论文

Spatial prediction of soil contamination based on machine learning: a review

期刊论文

Advancing agriculture with machine learning: a new frontier in weed management

期刊论文

A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

期刊论文

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

期刊论文

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

期刊论文

Optimization of kinetic mechanism for hydrogen combustion based on machine learning

期刊论文

Evaluation and prediction of slope stability using machine learning approaches

期刊论文

Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

期刊论文