Low Error Values Research Articles

Adsorption Performance and Mechanistic Pathways of Raw Powdered Pine Cone Toward the Removal of Dye and Cr(VI)

ABSTRACTThis work aims to valorize pine cones powder (PCP), an agricultural waste, as a bioadsorbent for the removal of a cationic dye, Rhodamine B (RhB), and hexavalent chromium Cr(VI) from an aqueous solution. The adsorbent was characterized by scanning electron microscopy (SEM), Fourier‐transformed infrared (FTIR), and X‐ray diffractometer (XRD) spectroscopy. Adsorption studies were carried out in a batch mode under different operating parameters like initial solution pH (2–11), adsorbent dosage (0.025–0.6 g), initial contaminant concentration (20–100 mg/L), temperature (283–328K), contact time (0–120 min), and ionic strength (NaCl, MgCl2, CuSO4, Na2SO4, and FeCl3). The optimum conditions for adsorption for Cr(VI) were; ([Cr(VI)] = 30 mg/L, pH = 2, adsorbent dose = 1.5 g/L, T = 25°C), and for RhB: ([RhB] = 30 mg/L, pH = 4.6, adsorbent dose = 4 g/L, T = 25°C). Under these conditions, the maximum adsorption capacities reached were 19.861 and 6.565 mg/g, for Cr(VI) and RhB, respectively. The inhibiting effect of the studied salts on RhB dye and metal ion removal is as follows: FeCl3 > CuSO4> MgCl2> Na2SO4> NaCl. The Freundlich isotherm described the best the adsorption of Cr (VI) and RhB onto PCP, since it gave the highest R2 values (R2 ≥ 0.983) associated with the lowest error values (χ2, RMSE, and Δq). The calculated Dubinin–Radushkevich mean energy (E) is less than 8 kJ/mol. It is 3.391 kJ/mol for Cr(VI) and 2.310 kJ/mol for RhB sorption, respectively, confirming the physical character of adsorption onto PCP sorbent. Experimental data for Cr(VI) and RhB ion adsorption onto PCP, fitted well the pseudo‐second‐order kinetic model (R2 ≥ 0.997). According to the thermodynamic analysis, the retention of Cr(VI) and RhB followed a physisorption, endothermic, and spontaneous process at all temperatures for Cr(VI), and at higher temperatures for RhB dye. These results suggest that raw PCP may be envisaged as a promising biosorbent for water remediation without the need of costly heat and activation processes as a cheap and sustainable adsorption process.

Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models

Geothermal energy is a sustainable resource for power generation, particularly in Yemen. Efficient utilization necessitates accurate forecasting of subsurface temperatures, which is challenging with conventional methods. This research leverages machine learning (ML) to optimize geothermal temperature forecasting in Yemen’s western region. The data set, collected from 108 geothermal wells, was divided into two sets: set 1 with 1402 data points and set 2 with 995 data points. Feature engineering prepared the data for model training. We evaluated a suite of machine learning regression models, from simple linear regression (SLR) to multi-layer perceptron (MLP). Hyperparameter tuning using Bayesian optimization (BO) was selected as the optimization process to boost model accuracy and performance. The MLP model outperformed others, achieving high R2 values and low error values across all metrics after BO. Specifically, MLP achieved R2 of 0.999, with MAE of 0.218, RMSE of 0.285, RAE of 4.071%, and RRSE of 4.011%. BO significantly upgraded the Gaussian process model, achieving an R2 of 0.996, a minimum MAE of 0.283, RMSE of 0.575, RAE of 5.453%, and RRSE of 8.717%. The models demonstrated robust generalization capabilities with high R2 values and low error metrics (MAE and RMSE) across all sets. This study highlights the potential of enhanced ML techniques and the novel BO in optimizing geothermal energy resource exploitation, contributing significantly to renewable energy research and development.

Evaluation of genotypic variations in the protein content of soybean through near infrared spectroscopy

Demand for soybean seeds with increased protein content is high in the international market. In this context, breeding soybean varieties targeting high protein content is the need of the day. In this paper, elite soybean germplasm accessions were screened for protein content. Eighty-eight soybean germplasm was evaluated for protein content through near infrared spectroscopy. A prediction model to determine the protein content of soybean germplasm through a non-destructive method was developed to calibrate the NIR. The protein content of 39 lines was analyzed in wet lab conditions and used to calibrate in NIRs between 1400 and 2400 nm at 2 nm intervals. High determination coefficient and low values of root mean square error (2.585) and standard error of prediction (2.832) confirmed the model’s utility for predicting the protein content of unknown samples. Accordingly, the protein content of 49 germplasm lines revealed that the genotypes LU96, TNAU20056, and SL525 depicted high values of 56.66, 55.51, and 54.48% protein content, respectively, but with fewer yields. The genotype RKS45 recorded a protein content of 45.33% with a single plant yield of 34.09 g, which can be further utilized for hybridization and selection. Thus, a non-significant correlation was observed between the protein content and single plant yield, suggesting that an increase in protein content will not directly influence the yield parameters. This paper provides a simple methodology to accurately determine the protein in a large set of samples in a short time, which helps in speed breeding programs.

Cutting Parameter Optimization of Single Point Cutting Tool of CNC Lathe Machine Using Machine Learning Algorithm

In this study, two machine learning models, Random Forest Regressor (RFR) and Decision Tree Regressor (DTR), were applied to predict surface roughness (SR) in machining processes (turning) based on key input parameters: spindle speed (SS), feed rate (FR), and depth of cut (DOC). The performance of both models was evaluated using R² (R-squared), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The Decision Tree Regressor achieved near-perfect accuracy with an R² score of 0.9999 and minimal error metrics. The Random Forest Regressor also performed exceptionally well, with an R² of 0.9988 and similarly low error values. Visual analyses, including residual and radar plots, confirmed the high accuracy of both models, with the Decision Tree Regressor slightly outperforming the Random Forest model. However, the Random Forest Regressor's ensemble structure provides better generalization and robustness, making it a more reliable model for larger or more complex datasets. This study concludes that both models are highly effective for predicting surface roughness, but the choice between them should depend on the specific trade-offs between accuracy and generalization needed in a given application.

Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting

This research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns. The proposed model integrates multi-layer graph convolutional networks (GCNs) to address dependencies in temporal and spatial traffic dynamics. Specifically, ISTGCN integrates graph convolutional layers and convolutional sequence learning layers within multiple spatiotemporal convolutional blocks. For capturing the temporal aspect, predictive graph modeling for road network traffic at particular time stamps is performed. To integrate the spatial information, graph convolution operations are applied. The proposed model was validated on real-life datasets, and the experimental results demonstrate that ISTGCN achieves significantly lower error values across key metrics—RMSE, MAE, and MAPE.

Predictive modeling of ash and moisture content in acid-treated walnut shells using artificial neural networks

ABSTRACT In this paper, the use of artificial neural networks (ANN) to predict changes in ash and moisture content (MC) of walnut shells after acidic pretreatment was investigated. The physicochemical and structural properties of walnut shells were analyzed, as well as the influential parameters on the changes of these studied variables. The study aimed to determine the changes in the walnut shells composition and evaluate the possibility of using the ANN model to predict the above-mentioned initial variables. The ANN model showed extremely high performance in modeling, with a coefficient of determination (R2) of 0.99 for ash and 0.90 for MC and low values of root mean square error (RMSE) and χ2, indicating the accuracy of the model in predicting the composition of walnut shells after acidic pretreatment. This study confirms the potential of ANN in accurately modeling changes in the composition of walnut shells and paves the way for further research on a wider range of crops and different pretreatments.

Inverse kinematics analysis of a wrist rehabilitation robot using artificial neural network and adaptive Neuro-Fuzzy inference system

This paper offers a comprehensive investigation into the forward and inverse kinematics of a wrist rehabilitation robot, utilizing the Denavit-Hartenberg method for forward kinematics (FK) and a geometric approach, as well as artificial neural networks (ANN) and adaptive Neuro-Fuzzy inference systems (ANFIS) for inverse kinematics (IK) analysis. While the geometric method entails precise parameter measurements and faces uncertainties, ANN and ANFIS are explored as potential remedies to enhance accuracy and robustness. Evaluating 11 different training functions sourced from existing literature, our study conducts a thorough assessment of their performance within an ANN network. We aim to pinpoint the most suitable training function for achieving optimal IK solutions in the context of a wrist rehabilitation robotic. Additionally, the ANFIS model, trained using Fuzzy C-Means (FCM), sets itself apart from Grid Partitioning (GP) and Subtractive Clustering (SC). Among the ANN training functions, Bayesian regularization with 5 hidden layers emerges as the most effective, yielding low root mean square error (RMSE) values of 0.003, 0.004, and 0.007 degrees for pronation/supination (P/S), abduction/adduction (AB/AD), and flexion/extension (F/E), respectively. Conversely, ANFIS, trained with FCM, demonstrates satisfactory yet less precise results, with RMSE values of 0.191, 0.082, and 0.165 degrees for P/S, AB/AD, and F/E, respectively. Despite its adequacy, ANFIS trails behind ANN, showcasing RMSE reductions of 98.4%, 95.1%, and 95.7% for P/S, AB/AD, and F/E angles, respectively. This study contributes to leveraging ANN and ANFIS for IK analysis in wrist rehabilitation robotics, highlighting the efficacy of ANN, particularly when employing Bayesian regularization, to enhance accuracy.

Applications of geographically weighted machine learning models for predicting soil heavy metal concentrations across mining sites

The accurate prediction of soil heavy metal contamination is crucial for the effective environmental management of abandoned mining areas. However, conventional machine learning models (CMLMs) often fail to account for the spatial heterogeneity of soil contamination, which limits their predictive accuracy. This study evaluated the performance of geographically weighted machine learning models (GWMLMs) in predicting soil Cd and Pb concentrations in abandoned mines in the Republic of Korea. We compared two GWMLMs (Geographically Weighted Random Forest and Geographically Weighted Extreme Gradient Boosting) with four CMLMs (Random Forest, Gradient Boosting, Light Gradient Boosting, and extreme Gradient Boosting). The data used in this study included soil samples from six abandoned mining sites with various geographical and soil input variables. The results showed that the GWMLMs consistently outperformed the CMLMs in predicting heavy metal contamination. For Cd predictions, GWMLMs exhibited on average 0.02 lower root mean square error and mean absolute error values, with a 0.26 increase in R2 values compared to CMLMs. Similarly, for Pb predictions, the GWMLMs showed 0.18 and 0.13 lower root mean square error and mean absolute error values, respectively, and a 0.17 increase in R2 relative to the CMLMs. The findings demonstrate the usefulness of GWMLMs for predicting the spatial distribution of soil heavy metals. SHapley Additive exPlanations analysis exhibited elevation and distance from abandoned mining sites as the most influential factors in predicting both Cd and Pb concentrations. This study highlights the value of GWMLMs that incorporate spatial heterogeneity into CMLMs for enhancing prediction accuracy and providing crucial insights for environmental management in mining-impacted regions.

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. We applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

Inactivation of deposited bioaerosols on food contact surfaces with UV-C light emitting diode devices.

The airborne transmission of infectious diseases and bioaerosol-induced cross-contamination pose significant challenges in the food, dairy, and pharma industries. This study evaluated the effectiveness of 279 nm UV-C LED irradiation for decontaminating bioaerosols, specifically containing microorganisms such as Escherichia coli (C3040- Kanamycin resistant), Salmonella Enteritidis (ATCC 4931), and Pseudomonas fragi (ATCC 4973), on food contact surfaces. Borosilicate glass, silicon rubber, and stainless steel (316L) surfaces were selected for experimentation for their usage in the food industry. A 50 µL cell suspension was aerosolized at 25 psi pressure using a 4-jet BLAM Nebulizer within a customized glass chamber and then deposited onto the surface of the coupons. The serial dilution approach was used for the microbial enumeration, followed by duplicate plating. With a low Root Mean Square Error (RMSE) and high R2 values, the biphasic kinetic model for UV-C inactivation curves of all three pathogens demonstrated the excellent goodness of fit parameters. At a UV-C dose of 6 mJ cm-2, glass surfaces showed the maximum microbial inactivation (i.e., 2.80, 3.81, and 3.56 log CFU/mL for E. coli, Salmonella, and P. fragi, respectively). Stainless steel and silicon rubber surfaces showed significant microbial inactivation, but log10 reductions observed were consistently lower than glass surface. Our research indicates that UV-C LEDs (279 nm) can effectively disinfect bioaerosols on food contact surfaces.IMPORTANCEFood safety is a major public health concern, with contaminated food causing serious illnesses. UV-C light, used for germicidal action, is effective in disinfecting surfaces and is not subject to the same strict legal restrictions as chemical disinfectants, simplifying compliance with food safety regulations. In this study, we evaluated the efficacy of UV-C (279 nm) LED systems for inactivation of surface-deposited bioaerosols of kanamycin-resistant Escherichia coli (C3040), Salmonella Enteritidis (ATCC 4931), and Pseudomonas fragi (ATCC 4973). The research outcomes can be used to develop UV-based surface disinfection systems to minimize the risk of foodborne illnesses and enhance safety in high-traffic food preparation areas.

UAV-Mounted RIS-Aided Multi-Target Localization System: An Efficient Sparse-Reconstruction-Based Approach

Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods.

Sustainable Spectrophotometric Quantification of Lansoprazole Through Mixed Hydrotropy Approach

Solubility is the property of a substance to dissolve in or form a homogeneous mixture with another substance. One of the most common problems associated with making a solution is poor solubility. There are many drugs that are sparingly soluble or insoluble in water, and lansoprazole is one of them. Aqueous solubility of lansoprazole is 0.05 mg/ml at room temperature. The current study aims to improve lansoprazole solubility using a mixed hydrotropy method. The purpose of the mixed hydrotropic solubilization approach is to increase the solubility of weakly water-soluble drugs in hydrotropic agent blends. To avoid the use of organic solvents, the mixed hydrotropy idea may be a good option. In this current research attempt, a novel method for spectrophotometric estimation of lansoprazole using a mixed solvent blend (containing 10% SC and 20% SB) as the solvent was developed. By observing the absorbances of the drug's standard solutions, the calibration curve for lansoprazole was drawn. The absorbances were measured at 275 nm compared to the corresponding reagent blanks. The percent label claims were found to be very near to 100, showing that the proposed approach is accurate. The suggested method estimates percent recoveries to be near 100 with significantly low percentage deviation and standard error values. As a result, the proposed process is simple, safe, and precise, and it does not require the use of harmful chemical solvents.

Advancing Geotechnical Evaluation of Wellbores: A Robust and Precise Model for Predicting Uniaxial Compressive Strength (UCS) of Rocks in Oil and Gas Wells

This study examines the efficacy of various machine learning models for predicting the uniaxial compressive strength (UCS) of rocks in oil and gas wells, which are essential for ensuring wellbore stability and optimizing drilling operations. The investigation encompasses Linear Regression, ensemble methods (including Random Forest, Gradient Boosting, XGBoost, and LightGBM), support vector machine-based regression (SVM-SVR), and multilayer perceptron artificial neural network (MLP-ANN) models. The results demonstrate that XGBoost and Gradient Boosting offer superior predictive accuracy for UCS in drillability, as indicated by low Mean Absolute Percentage Error (MAPE) values of 3.87% and 4.18%, respectively, and high R2 scores (0.8542 for XGBoost). These models emerge as optimal choices for UCS prediction focused on drillability, offering increased accuracy and reliability in practical engineering scenarios. Ensemble methods and MLP-ANN emerge as frontrunners, providing valuable tools for improving wellbore stability assessments, optimizing drilling parameter selection, and facilitating informed decision-making processes in oil and gas drilling operations. Moreover, this study lays a foundation for further research in drillability-centred predictive modelling for geotechnical parameters, advancing our understanding of rock behaviour under drilling conditions.

Structural damage detection using a sensitivity-based model updating approach with autocorrelation function data

ABSTRACT This study presents a novel sensitivity equation for estimating structural parameters using autocorrelation function (ACF) data of dynamic responses. The proposed method reduces the need for extensive excitation frequencies, making structural parameter identification more practical and cost-effective, especially for large-scale structures. Furthermore, due to the quadratic relationship between ACF and the structural dynamic response, this approach exhibited greater sensitivity to damage than time-history-based methods. The method’s effectiveness was evaluated using simulated data from truss and offshore jacket platform models. Incomplete measurements were addressed by using partially measured structural characteristics without model reduction and data expansion processes. Measurement and modelling errors were simulated by adding uniformly distributed random error into the damaged structure data. The low values of the coefficient of variation indicated the method’s robustness against these errors. The accuracy of the method was confirmed through low root mean square error (RMSE) values and high variance accounted for (VAF) values.

Machine learning-based clustering analysis of the banking green credit for the renewable energy industry

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

ESP32 and MAX30100 with Chebyshev Filter for Enhanced Heart and Oxygen Measurement

Health monitoring is important in the technology and information era. A health monitoring device must possess high accuracy in monitoring an individual's health. The MAX30100 sensor still exhibits low accuracy and requires improvements to enhance its precision. This study proposes a remote health monitoring system based on a MAX30100 sensor for heart rate and oxygen saturation detection. The digital signal processing method uses the Chebyshev II filter on PPG to reduce noise, and the RSA algorithm is employed to enhance data security. The results of testing the MAX30100 sensor value without a filter produced the lowest error value of 0.97%, the highest 6.59% for BPM, the lowest error value of 1.88%, and the highest error of 2.66% for SpO2. The MAX30100 sensor with the Chebyshev II filter that the author proposed has the highest level of accuracy with a low error value compared to previous tests, with the lowest error value of 0.23% and the highest 0.99% for BPM and the lowest error value of 0% and the highest error of 0.2% for SpO2. The RSA algorithm ensures secure data transmission from data modification by eavesdroppers. The average total time required by the system is 542.9 ms.

Machine Learning for the Genomic Prediction of Growth Traits in a Composite Beef Cattle Population.

The adoption of genomic selection is prevalent across various plant and livestock species, yet existing models for predicting genomic breeding values often remain suboptimal. Machine learning models present a promising avenue to enhance prediction accuracy due to their ability to accommodate both linear and non-linear relationships. In this study, we evaluated four machine learning models-Random Forest, Support Vector Machine, Convolutional Neural Networks, and Multi-Layer Perceptrons-for predicting genomic values related to birth weight (BW), weaning weight (WW), and yearling weight (YW), and compared them with other conventional models-GBLUP (Genomic Best Linear Unbiased Prediction), Bayes A, and Bayes B. The results demonstrated that the GBLUP model achieved the highest prediction accuracy for both BW and YW, whereas the Random Forest model exhibited a superior prediction accuracy for WW. Furthermore, GBLUP outperformed the other models in terms of model fit, as evidenced by the lower mean square error values and regression coefficients of the corrected phenotypes on predicted values. Overall, the GBLUP model delivered a superior prediction accuracy and model fit compared to the machine learning models tested.

Penentuan jumlah produksi kimbab berdasarkan jumlah permintaan dan persediaan menggunakan metode Fuzzy Mamdani

Nanda Kitchen is a home-based cafe that produces various kinds of Korean specialties, specifically kimbab. The large demand for kimbab signifies that Nanda Kitchen is frequently engaged to the stockouts of raw material supplies. Thus, kimbab production is unable to fullfil consumer’s demand. The intention of this research is to ascertain the appropriate amount of kimbab production. Effective demand forecasting is required to fullfil customer’s demand by sufficient raw material supplies. Demand forecasting was carried out using POM-QM for Windows with Moving Average (n=3) and Exponential Smoothing (alpha 0.3 and 0.5). The lowest error value is taken to implement Fuzzy Mamdani (MATLAB). Exponential Smoothing (?=0.5) is most proper method for forecasting demand (error value of 7.6%). Nanda Kitchen needs to produce 1460 kimbabs with input demand of 1473 and raw material supply of 2470 based on Fuzzy Mamdani.

An experimental and ANN analysis of ammonia energy integration in biofuel powered low-temperature combustion engine to enhance cleaner combustion

In the pursuit of global net-zero emissions targets and cleaner combustion technologies, researchers are increasingly turning to innovative solutions. Ammonia, as a carbon-free fuel, holds promise for achieving low emissions and efficient combustion. This study explores the potential synergies of various ammonia shares (20 %, 40 %, and 60 %) combined with Citronella biofuel in a Low-Temperature Combustion (LTC) Reactivity Controlled Compression Ignition (RCCI) engine. Comparative analysis between a 40 % ammonia blend Citronella biofuel (A40) and a Standard Diesel (SDL) in an RCCI engine demonstrates significant reductions in oxides of nitrogen (by 17 %), carbon dioxide (by 6.6 %), and smoke (by 9.8 %), along with a notable enhancement in Brake Thermal Efficiency (BTE) by 5.4 % for A40. The validation of results using an Artificial Neural Network (ANN) model shows robust coefficients of determination (97 %), high R values (0.9945–0.9995), and low Root Mean Square Error values (ranging from 0.0219 to 0.0483). These findings strongly support the efficacy of the A40 blend, positioning it as a promising and viable alternative fuel for achieving cleaner combustion in LTC-RCCI engines.

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.