Input Variables Research Articles

Global Sensitivity Analysis and Surrogate Models for Evaluation of Limit States in Steel Truss Structures

This article presents the global sensitivity analysis of the serviceability limit state of a steel truss using Monte Carlo simulations. The focus is on the probabilistic assessment of deflection, with failure probability defined as the likelihood of exceeding the deflection limit. Deflection is computed using the beam finite element method. A surrogate model is introduced to reduce computational costs. By integrating the surrogate and original models, significant CPU cost reductions are achieved. Furthermore, classical Sobol sensitivity analysis is used to examine the model outputs and analyze the significance of member loading and stiffness on the deflection. This study advances the use of surrogate models in global sensitivity analysis, enhancing computational efficiency and the understanding of interactions between input variables in the reliability assessment of steel truss structures.

Uncertainty-informed selection of CMIP6 Earth system model subsets for use in multisectoral and impact models

Abstract. Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Performance of insulators under variation of pollution, inclined angle, and temperature based on the design of experiment

The environmental conditions significantly impact the performance of the transmission line insulators. The current work investigated some variables that influence the flashover and breakdown voltages, such as insulator pollution (P), insulator inclined angles with the cross-arm (A), and insulator temperature (T). A prediction equation can be developed using Box-Behnken Design (BBD) to relate the input variables (P, A, and T) and the flashover voltage (VFO). BBD is a rotatable second-order design requiring three levels of each variable, influencing a specific experiment response. A statistical analysis was carried out to identify the significant variables affecting voltage VFO. So, the main objective of the work is to investigate the effect of P, A, and T on the VFO and use Box Behnken Design (BBD) to predict the value of VFO when the information about P, A, and T is available without requiring any experimental works. The results illustrated a prediction equation that facilitates the prediction of the voltage VFO if new values of the input parameters are known. The prediction error of the constructed equation was less than 5 % for a total number of 21 new data samples used to test and verify the robustness of the prediction equation.

Leveraging Deep Learning and Generative AI for Predicting Rheological Properties and Material Compositions of 3D Printed Polyacrylamide Hydrogels.

Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G') and loss (G") moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G' and G". We employed a multilayer perceptron (MLP) and successfully predicted G' and G" from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R2 value to be 0.89. Next, we adopted two generative DL models named variational autoencoder (VAE) and conditional variational autoencoder (CVAE) to learn data patterns and generate constituent compositions. With these generative models, we produced synthetic data with the same statistical distribution as the real data of actual hydrogel fabrication, which was then validated using Student's t-test and an autoencoder (AE) anomaly detector. We found that none of the seven generated gel constituents were significantly different from the real data. Our trained DL models were successful in mapping the input-output relationship for the 3D-printed hydrogel substrates, which can predict multiple variables from a handful of input variables and vice versa.

Modelling and optimizing the impact resistance of engineered cementitious composites with Multiwalled carbon nanotubes using response surface methodology

Engineered Cementitious Composites (ECC) are highly regarded in construction owing to their tensile ductility and crack control capabilities, making them suitable for various structural applications. The accumulation of multi-walled carbon nanotubes (MWCNTs) further enhances their mechanical properties. However, there’s a significant knowledge gap concerning MWCNTs-ECC impact resistance. The objective of this study is to tackle the challenges associated with evaluating, optimizing, and predicting MWCNTs-ECC impact resistance to ensure its safe and widespread use in critical infrastructure by applying response surface methodology (RSM). Moreover, the 13 mixtures of ECC combined with several quantities of PVA fiber and MWCNTs as input elements were utilized to calculate the first (E1) and final (E2) impact energies. The findings demonstrated that the MWCNTs-ECC combinations’ impact resistance improved as the input ingredient concentrations increased. Besides, the optimum E1 and E2 of ECC combined with 1% of PVA fiber were noted by 1398 Joules and 12,956 Joules at 0.065% of MWCNTs on 28 days respectively. Furthermore, Response prediction models for E1 and E2 were created, and after being validated with an analysis of variance (ANOVA), it was determined that they had high R2 readings of 99.30% and 99.07%, correspondingly. The optimization process produced an ideal number of input variables for MWCNTs and PVA fiber, respectively, of 0.066% and 1%, with a desirability value of 100%. Moreover, it is recommended that the usage of 0.066% of MWCNTs in ECC combined with 1.0–1.50% PVA fiber provides optimum results for the construction industry.

Influence of manufacturing errors on the aerodynamic working stability of axial flow compressors

In turbomachinery, manufacturing errors in blades have been found to impact aerodynamic performance. This paper aims to investigate the effect of manufacturing errors on the stability of compressor operation. To this end, a geometric uncertainty reduced-order model is developed to generate the normal profile error of rotor blades based on specific tolerances, considering the simultaneous variation of blade parameters during manufacturing. The stability margin improvement of the compressor is then calculated using computational fluid dynamics (CFD), and a neural network is employed to predict the relationship between the uncertainty input variables and the stability margin improvement. Finally, a large number of samples are generated using the Quasi-Monte Carlo method, and Sobol sensitivity analysis is performed. According to the results, manufacturing errors can reduce the stability margin by an average of 0.9% and up to 3% in the limiting case. The parameters that have the greatest impact on the stability of the compressor are the blade chord length of the hub section and the maximum thickness of the tip section. These two parameters affect blade tip blockage by influencing spanwise secondary flow on the suction surface. Additionally, the decrease in maximum thickness at the tip section affects the separation of the boundary layers on the suction surface, resulting in additional effects.

Feasibility study of data-driven wall interference correction framework for subsonic wind tunnel

Although the classical method is widely used for wall interference correction in wind tunnel testing, its reliability and accuracy for complex and unconventional geometries are rather limited. Studies on the evaluation of wall interference and the improvement of correction methods are desirable to enhance the reliability and generality for various geometric configurations. This study proposes a wall interference correction framework based on a deep neural network (DNN) ensemble using data obtained from the numerical panel method. The panel method is validated by comparing the results with those of Reynolds-averaged Navier-Stokes simulations. An automated process was established to generate a large amount of training data, and 600,000 datasets were generated based on the geometric parameters of the wind tunnel, test model, and angles of attack. The input variables of the DNN were determined through sensitivity analysis of the data. To alleviate the randomness of the initial weights and data distribution in the generation process of the DNN model, 20 DNNs with the same multi-layer perceptron structure were trained, and a DNN ensemble model was constructed using five ensemble members with high predictability. The accuracy of the DNN-ensemble based correction models were evaluated by comparing the correction results for the testing data.

A machine learning approach to fill gaps in dendrometer data

Abstract Key message The machine learning algorithm extreme gradient boosting can be employed to address the issue of long data gaps in individual trees, without the need for additional tree-growth data or climatic variables. Abstract The susceptibility of dendrometer devices to technical failures often makes time-series analyses challenging. Resulting data gaps decrease sample size and complicate time-series comparison and integration. Existing methods either focus on bridging smaller gaps, are dependent on data from other trees or rely on climate parameters. In this study, we test eight machine learning (ML) algorithms to fill gaps in dendrometer data of individual trees in urban and non-urban environments. Among these algorithms, extreme gradient boosting (XGB) demonstrates the best skill to bridge artificially created gaps throughout the growing seasons of individual trees. The individual tree models are suited to fill gaps up to 30 consecutive days and perform particularly well at the start and end of the growing season. The method is independent of climate input variables or dendrometer data from neighbouring trees. The varying limitations among existing approaches call for cross-comparison of multiple methods and visual control. Our findings indicate that ML is a valid approach to fill gaps in individual trees, which can be of particular importance in situations of limited inter-tree co-variance, such as in urban environments.

PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Sugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS is a combined method of two systems, namely a fuzzy logic system and an artificial neural network system. This research consists of data collection, ANFIS system design, ANFIS training, ANFIS testing, accuracy calculation, and result analysis. The prediction system for the amount of sugar production is designed to predict the variable which is the amount of sugar production in the year using the input variables (sugarcane harvested area in year ), (amount of sugarcane in year ), (average of yield in year ), and (number of milling days in year ). The experiments in this research used variations of the type of membership function and the number of membership functions. The best model obtained in this research is a model with a difference between two sigmoidal membership functions and a product of two sigmoidal membership functions with a total of 2 membership functions for each input variable. Both models have the same Mean Absolute Percentage Error (MAPE) value, which is 1.79% in the training process and 4.82% in the testing process.

Quantization‐Based Latin Hypercube Sampling for Dependent Inputs With an Application to Sensitivity Analysis of Environmental Models

ABSTRACTNumerical models are essential for comprehending intricate physical phenomena in different domains. To handle their complexity, sensitivity analysis, particularly screening is crucial for identifying influential input parameters. Kernel‐based methods, such as the Hilbert‐Schmidt Independence Criterion (HSIC), are valuable for analyzing dependencies between inputs and outputs. Implementing HSIC requires data from the original model, which leads to the need of efficient sampling strategies to limit the number of costly numerical simulations. While, for independent input variables, existing sampling methods like Latin Hypercube Sampling (LHS) are effective in estimating HSIC with reduced variance, incorporating dependence is challenging. This article introduces a novel LHS variant, quantization‐based LHS (QLHS), which leverages Voronoi vector quantization to address dependent inputs. The method provides good coverage of the range of variations in the input variables. The article outlines expectation estimators based on QLHS in various dependency settings, demonstrating their unbiasedness. The method is applied to several models of growing complexities, first on simple examples to illustrate the theory, then on more complex environmental hydrological models, when the dependence is known or not, and with more and more interactive processes and factors. The last application is on the digital twin of a French vineyard catchment (Beaujolais region) to design a vegetative filter strip and reduce water, sediment, and pesticide transfers from the fields to the river. QLHS is used to compute HSIC measures and independence tests, demonstrating its usefulness, especially in the context of complex models.

Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP

The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction accuracy of blasting block size, the measured data of Beskuduk open-pit coal mine is used as training and testing samples. Seven factors including rock tensile strength, rock compressive strength, and blast hole spacing are selected as input variables of the prediction model. The average size of blasting fragmentation X50 is used as the output variable of the prediction model. The kernel principal component analysis (KPCA) is adopted to reduce the dimensionality of the input variables. The beetle antennae search algorithm (BAS) is selected to optimize the parameters of the initial weights and thresholds of the back propagation (BP) neural network. Finally, prediction model of blasting fragmentation in open-pit coal mine based on KPCA-BAS-BP is established. The results show that the average relative error of the model is 1.77%, and the root mean square error is 1.52%. Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.

COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.

Strength of viscous subduction interfaces: A global compilation

The long- and short-term dynamics of subduction zones are strongly affected by interface rheology. Current estimates of interface viscosity are based on endmember flow laws or simple mixing models, but the variation of possible materials on the subduction interface theoretically permits viscosity variations of up to five orders of magnitude. To better constrain the range of strength along deep/viscous subduction interfaces, we compiled a global database of rock types and block-and-matrix distributions in exhumed deep subduction interface mélange shear zones. We applied numerical shear experiments to each shear zone to quantify their bulk shear strengths/viscosities. Our results show that natural subduction shear zones have viscosities ranging from ∼1018 to 1020 Pa⋅s and shear strengths of ∼1−100 MPa. The variation among them is dominantly controlled by temperature, but even for shear zones deformed at the same temperature, variations of up to a factor of 50 are observed. These variations are attributed to differences in matrix composition, shear zone width, and block distributions. Our results are consistent with paleopiezometric and force-balance−derived estimates of interface strength, and they confirm the importance of variation in geological inputs and their spatial distribution along the interface for subduction dynamics.

Contrastive adaptation effects along a voice-nonvoice continuum.

Adaptation to the environment is a universal property of perception across all sensory modalities. It can enhance the salience of new events in an ongoing background and helps maintain perceptual constancy in the face of variable sensory input. Several contrastive adaptation effects have been identified using sounds within the categories of human voice and musical instruments. The present study investigated whether such contrast effects can occur between voice and nonvoice stimulus categories. A 10-step continuum between "voice" (/a/, /o/, or /u/ vowels) and "instrument" (bassoon, horn, or viola) sounds was generated for each of the nine possible pairs. In each trial, an adaptor, either a voice or instrument, was played four times and was followed by a target from along the appropriate continuum. When trials with voice and instrumental adaptors were grouped into separate blocks, strong contrastive adaptation effects were observed, with the target more likely to be identified as a voice following instrumental adaptors and vice versa (Experiment 1). The effects were not observed for visual image adaptors (Experiment 2). The effects were somewhat larger when the adaptors and the target were presented to the same than to different ears, but significant adaptation was observed in both conditions, suggesting contributions of central mechanisms, following binaural integration (Experiment 3). The effect accumulated when the same type of adaptor was presented consecutively and persisted following the end of the adaptors (Experiment 4). The discovery of voice-nonvoice contrastive pairs opens the possibility of studying perceptual or neuronal voice selectivity while keeping acoustic features constant. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prediction of Performance and Emissions Diesel Engines Fueled-Biodiesel Using Artificial Neural Network (ANN) Resilient Backpropagation Algorithm (Rprop)

In order to increase energy security and improve environmental quality, the Indonesian Goverment set a target of 23% renewable energy mix in 2025, one of which is the Mandatory Bioediesel Program. A higher biodiesel blending ratio will affect the performance and emissions of diesel engines because biodiesel is chemically different from diesel oil. Research related to the prediction of diesel engine performance and emissions using Artificial Neural Network (ANN) has been conducted, but the author sees a research opportunity for the implementation of the ANN Resilient Backpropagation (Rprop) algorithm. The data used to create the ANN model prediction was secondary data from previous research. The model designed multi input and multi output (MIMO) with 4 input variables and 7 output variables. Model building done by varying the number of neurons and hidden layers. Model evaluation selected based on the largest coefficient of determination parameter R2 and the smallest RMSE or MAPE. The results showed that the ANN single layer 4-20-7 network architecture is the best model for predicting diesel engine performance and emissions with test data R2 , RMSE and MAPE of 0.962532, 6.699428 and 6.0% respectively, while for overall data testing has a performance of 0.982869, 3.908542 and 4.3%. The results also show that based on the ANN prediction results, the increasing biodiesel ratio can increase NOx emissions and decrease HC, CO and CO emissions2 . In terms of performance, the addition of biodiesel can increase BSFC and BP and decrease BTE. The results also show that the addition of ZnO concentration can reduce emissions while in terms of performance it will increase BTE and reduce BSFC and BP.

Deep Information Retention Network‐Enabled Data Modeling for Key Quality Indicator Prediction in the Chemical Industry

ABSTRACTDeep learning has attracted widespread attention in data modeling and key quality indicator prediction in the chemical industry. However, traditional deep learning networks usually distort the original data distribution due to the superposition effect of multiple layers of nonlinear activation functions. In this case, multivariate statistical learning techniques present an avenue to reveal the intrinsic relationship of the data by combining the linear trends between input and predictor variables. To comprehensively capture data features from multiple perspectives, this study proposes a deep learning‐based data modeling network called the information retention unit (IRU). This network combines intrinsic attributes to partial least squares (PLS) and autoencoder (AE) modalities, thus engendering an adaptive response to the complex linear and nonlinear data features. Furthermore, multiple IRUs can be stacked to construct a deep information retention network (DIRN), which enhances the robust extraction of deep data features. Finally, the effectiveness of the proposed network is validated through its prediction application on a dataset obtained from a real‐world chemical industrial process. This method combines multivariate statistical learning techniques based on deep learning, providing an innovative and practical solution for data analysis and prediction in the chemical industry.

Predictive and experimental assessment of chloride ion permeation in concrete subjected to multi-factorial conditions using the XGBoost algorithm

Chloride ions infiltrate concrete structures, causing corrosion of the steel reinforcement, which creates concrete spalling and decreases load-bearing capacity, ultimately leading to structural failure. Thus, it is essential to comprehend the diffusion process of chloride ions in concrete. Current models used to predict the chloride ion diffusion in concrete are hindered by poor accuracy due to their simplistic consideration of variables and the complex interplay of environmental and material factors affecting key parameters such as surface chloride concentration (Cs) and the chloride diffusion coefficient (D). These influences are challenging to capture with simple linear or non-linear relationships, and traditional machine learning models often overfit training data and underperform with new data. To address these challenges, this study presents physical model experiments conducted to explore the diffusion process of chloride ions under multi-field coupling conditions, examining chloride ion concentrations across different concrete layers under varying environmental temperatures (T), humidities (h), erosion times (t), water-cement ratios (W/C), and volumes of coarse aggregates (v). This study reveals the distribution patterns of chloride ions within concrete layers. An XGBoost machine learning predictive model was developed using environmental temperature, humidity, erosion time, water-cement ratio, and coarse aggregate volume as input variables, with Cs and D as output variables. The results show that when the water-cement ratio reaches 0.5 in high humidity, the SHAP value rises to 0.015, enhancing chloride diffusion. As erosion time exceeds 200 days and temperature surpasses 38 °C, the SHAP value peaks at 0.02. Additionally, when coarse aggregate volume is between 0.45 and 0.60, temperature changes have little effect on Cs. Additionally, a graphical user interface was developed for modeling Cs and D to enhance practical usability.

Prediction of electron-solid interaction parameters using machine learning.

Electron backscattering coefficient and electron-stopping power are essential concepts in many disciplines, from radiation to materials science, semiconductor manufacturing, and space exploration. They enable precise calculations, measurements, and simulations of electron interactions with matter, which contribute to advancing science, technology, and safety in a variety of applications. The availability of these data is fundamental to scientific research to validate hypotheses, conduct experiments, and explore new theories. A relatively novel machine learning approach has demonstrated notable success in enhancing data quality and completeness, significantly contributing to the facilitation of data discovery. Using fundamental material property data, the stacking ensemble machine learning (EML) technique was established in this study to generate electron-solid interaction parameters for any target material over a wide range of energies. The final stacking EML was built using the base and meta learners bagging regressor (BR), K-nearest neighbors (k-NN), random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGB). In this study, two publicly available databases with a total of 4030 data points were used. Training datasets have 785 and 525 data points for electron backscattering coefficient and stopping power, respectively, whereas testing datasets contain 262 and 175 data points. Fivefeatures were used as input variables to train different individual algorithms and their combinations. On both the training and test datasets, the model was evaluated using different error metrics, including R-squared (R2), mean-absolute-error (MAE), root-mean-squared-error (RMSE), and mean-absolute-percentage-error (MAPE). Our model evaluation tests revealed that combining RF and XGB with a k-NN meta-learner outperformed other algorithms. The analysis of error metrics demonstrated a very close fit to all samples in each training dataset. Furthermore, predictions made by the model on unseen test data indicated accurate estimations of new backscattering and stopping power data. The developed model achieved high prediction accuracy for various target materials across the broad electron energy spectrum. The outcomes demonstrate the effectiveness of machine learning methodology and the chosen models' suitability for addressing substantial physics challenges.

Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy

As industrial development drives the increasing demand for steel, accurate estimation of the material’s fatigue strength has become crucial. Fatigue strength, a critical mechanical property of steel, is a primary factor in component failure within engineering applications. Traditional fatigue testing is both costly and time-consuming, and fatigue failure can lead to severe consequences. Therefore, the need to develop faster and more efficient methods for predicting fatigue strength is evident. In this paper, a fatigue strength dataset was established, incorporating data on material element composition, physical properties, and mechanical performance parameters that influence fatigue strength. A machine learning regression model was then applied to facilitate rapid and efficient fatigue strength prediction of ferrous alloys. Twenty characteristic parameters, selected for their practical relevance in engineering applications, were used as input variables, with fatigue strength as the output. Multiple algorithms were trained on the dataset, and a deep learning regression model was employed for the prediction of fatigue strength. The performance of the models was evaluated using metrics such as MAE, RMSE, R2, and MAPE. The results demonstrated the superiority of the proposed models and the effectiveness of the applied methodologies.

Machine learning based prediction of biogas generation from municipal solid waste: A data-driven approach

This study aims to imply different machine learning (ML) (artificial neural network (ANN), linear regression, XGBoost, random forest (RF), support vector machine (SVM)) models to predict and optimize biogas production yield from organic fractions of municipal solid waste (MSW). The data set for six key input variables, including moisture content, C/N ratio, lignocellulose content and MSW age was analyzed and processed using ML models. Further, data exploratory analysis (EDA) analysis was performed using various steps such as data preprocessing, feature selection, train-test-validation splitting, model testing and evaluation. The results revealed that XGBoost and RF regression model exhibited superior performance efficacy. Both the models achieved a higher R2 values of 0.88 and 0.68, and low root mean square error (RMSE) values of 305 and 496, respectively. Furthermore, feature importance analysis was performed to identify the relative significance of input variables in biogas prediction. SVM model revealed significant contributions of moisture content (20.86 %), cellulose (60.21 %), hemicellulose (34.91 %), lignin content (62.84 %), and MSW age (17.23 %) in predicting biogas production. The findings of the study can be a resource for techno-crates/researchers to develop an efficient ML based decision-making tools for accurate predictions and process optimization.