Optimization of CO2 absorption rate for environmental applications and effective carbon capture

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144707 - 144707

Published: Jan. 1, 2025

Language: Английский

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Optimization algorithms for modeling conversion and naphtha yield in the catalytic co-cracking of plastic in HVGO

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106958 - 106958

Published: Feb. 1, 2025

Language: Английский

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Application of machine learning techniques to model a full-scale wastewater treatment plant with biological nutrient removal

Journal of environmental chemical engineering, Journal Year: 2022, Volume and Issue: 10(3), P. 107430 - 107430

Published: March 8, 2022

Language: Английский

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The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction

Hydrological Sciences Journal, Journal Year: 2021, Volume and Issue: 67(2), P. 161 - 174

Published: Nov. 30, 2021

This paper focuses on the development of a robust accurate streamflow prediction model by balancing abilities exploitation and exploration to find best parameters machine learning model. To do so, simulated annealing (SA) algorithm is integrated with mayfly optimization (MOA) as SAMOA determine optimal hyper-parameters support vector regression (SVR) overcome weakness MOA method. The proposed method compared classical SVR hybrid SVR-MOA. examine accuracy selected methods, monthly hydroclimatic data from Jhelum River Basin used predict basis RMSE, MAE, NSE, R2 indices. Test results show that SVR-SAMOA outperformed SVR-MOA models. reduced errors models decreasing RMSE MSE 21.4% 14.7% 21.7% 15.1%, respectively, in test stage.

Language: Английский

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Development of integrative data intelligence models for thermo-economic performances prediction of hybrid organic rankine plants

Energy, Journal Year: 2024, Volume and Issue: 292, P. 130503 - 130503

Published: Feb. 1, 2024

Language: Английский

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Smartphone digital image colorimetry couple with chemometric approach for determination of boron in nuts prior to deep eutectic solvent liquid–liquid microextraction: a first application of hybrid chemometrics in SDIC

Analytical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

In this research, a green approach utilizing deep eutectic solvent liquid-liquid microextraction is combined with smartphone digital image colorimetry for the determination of boron in nut samples. A camera was used to capture analyte extract located custom-made colorimetric box. Using ImageJ software, images were split into RGB channels, channel identified as optimum. The distance between cuvette containing and detection determined be 8 cm, while brightness light source 30%. All obtained at 585 nm monochromatic positioned background source. extraction achieved 450 µL 1:4 choline-chloride phenol mole ratio within 60 s another minute centrifugation. limits quantification found 0.02 0.06 µg mL-1, respectively. method linearity, indicated by relative coefficient, greater than 0.9955 standard deviations below 5.4%. Lastly, application chemometrics form artificial intelligence (AI)-based models hybrid machine learning methodologies has been incorporated SDIC quantitative simulation parameters. results gathered showed that these are capable predicting

Language: Английский

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Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

International Journal of Environmental Research and Public Health, Journal Year: 2022, Volume and Issue: 19(2), P. 738 - 738

Published: Jan. 10, 2022

Reliable modeling of novel commutative cases COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark health-related policies. The current study proposes multi-regional CCC first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), generalized least squares method (ARIMAGLS) ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), ensemble (EML) were applied to second predict effect knowledge (FK) during pandemic. For this purpose, augmented Dickey-Fuller (ADF) Phillips-Perron (PP) unit root tests, autocorrelation function (ACF), partial (PACF), Schwarz information criterion (SIC), residual diagnostics considered in best model cumulative across multi-region countries. Seven performance criteria used evaluate accuracy models. obtained results justified both types model, ARIMAGLS demonstrating superiority other Among DL analyzed, LSTM-M1 emerged as most reliable estimation RF LSTM attaining more than 80% prediction accuracy. While EML proved merit 96% outcomes two scenarios indicate time series further decision making FK.

Language: Английский

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A framework based on multivariate distribution-based virtual sample generation and DNN for predicting water quality with small data

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 368, P. 133227 - 133227

Published: July 21, 2022

Language: Английский

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Earth skin temperature long-term prediction using novel extended Kalman filter integrated with Artificial Intelligence models and information gain feature selection

Sustainable Computing Informatics and Systems, Journal Year: 2022, Volume and Issue: 35, P. 100721 - 100721

Published: March 10, 2022

Language: Английский

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Interpretation the Influence of Hydrometeorological Variables on Soil Temperature Prediction Using the Potential of Deep Learning Model

Knowledge-Based Engineering and Sciences, Journal Year: 2023, Volume and Issue: 4(1), P. 55 - 77

Published: May 1, 2023

The importance of soil temperature (ST) quantification can contribute to diverse ecological modelling processes as well for agricultural activities. Over the literature, it was evident that supports more than 95% living habitats and food production on earth, this demand will increase 500 years’ times in expected consumption 2060. This paper aims analyses contrastive approach predict ST a certain region with help different machine learning models, including Random Forest (RF), Support Vector, Neural Network (NN), Linear Regression (LR) Long Short-Term Memory (LSTM). study utilized hourly humidity, dew point, rainfall, solar radiation, barometer readings formulation models. Various performance criteria were employed evaluate prediction skills models results depicted promising ability belong LSTM despite acceptable accuracy achieved by other outcomes revealed model attained lowest root mean square error (RMSE = 3.3255) decreased average 6% regards NN 3.4796), SVM 3.5766), RF 3.8128), improved LR 15%. is compliance latest industry standards allows low-cost experimental performances low powered edge computing devices.

Language: Английский

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