Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach DOI Open Access
Joanna Rosik, Sylwia Stegenta-Dąbrowska

Published: April 28, 2024

Although composting has many advantages in the treatment of organic waste, there are still problems and challenges associated with emissions, like NH3, VOCs, H2S, as well greenhouse gases such CO2, CH4, N2O. One promising approach to enhancing conditions is used novel analytical methods bad on artificial intelligence. To predict optimize emissions (CO, NH3) during process kinetics thought mathematical models (MM) machine learning (ML) were utilized. Data about everyday from laboratory compost’s biochar different incubation (50, 60, 70 °C) doses (0, 3, 6, 9, 12, 15% d.m.) for MM ML selections training. not been very effective predicting (R2 0.1 - 0.9), while acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0,71, CO2:0,81, NH3:0,95, H2S:0,72)) decision tree (DT, RPART; CO:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For first time CO H2S demonstrated. Further research a semi-scale field study needed improve developments models.

Language: Английский

The role of artificial intelligence in the management of liver diseases DOI Creative Commons
Ming‐Ying Lu, Wan‐Long Chuang, Ming‐Lung Yu

et al.

The Kaohsiung Journal of Medical Sciences, Journal Year: 2024, Volume and Issue: 40(11), P. 962 - 971

Published: Oct. 23, 2024

Abstract Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct‐acting antivirals (DAA) against C (HCV) have reshaped epidemiology chronic liver diseases. However, some aspects management diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite high efficacy DAAs, successful antiviral therapy does not eliminate risk hepatocellular carcinoma (HCC), highlighted need for cost‐effective identification high‐risk populations HCC surveillance tailored treatment strategies these populations. The accessibility high‐throughput genomic data has accelerated development precision medicine, emergence artificial intelligence (AI) led new era medicine. AI learn from complex, non‐linear identify hidden patterns within real‐world datasets. combination multi‐omics approaches facilitate disease diagnosis, biomarker discovery, prediction prognosis. algorithms been implemented in various aspects, including non‐invasive tests, predictive models, image interpretation histopathology findings. support clinicians decision‐making, alleviate clinical burdens, curtail healthcare expenses. In this review, we introduce fundamental concepts machine learning review role

Language: Английский

Citations

3
Electric heating promotes sludge composting process: Optimization of heating method through machine learning algorithms DOI

Youzhao Wang,

Feng Ma, Tong Zhu

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 382, P. 129177 - 129177

Published: May 15, 2023

Language: Английский

Citations

7
Microbiome data analysis via machine learning models: Exploring vital players to optimize kitchen waste composting system DOI
Shang Ding,

Liyan Jiang,

Jiyuan Hu

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 388, P. 129731 - 129731

Published: Sept. 11, 2023

Language: Английский

Citations

7
Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning DOI Open Access
Xuyuan Zhang, Ying‐Qing Guo, Haoran Luo

et al.

Water, Journal Year: 2024, Volume and Issue: 16(7), P. 1018 - 1018

Published: April 1, 2024

The rapid identification of the amount and characteristics chemical oxygen demand (COD) in influent water is critical to operation wastewater treatment plants (WWTPs), especially for WWTPs face with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments soluble (SCOD) nitrogen degradation three established machine learning (ML) models accurate prediction variation SCOD. results indicate that four different kinds components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes majority internal carbon sources endogenous denitrification, whereas C4 (230 275/350 tyrosine-like substances) crucial readily biodegradable SCOD composition according models. Furthermore, gradient boosting decision tree (GBDT) algorithm achieved higher interpretability generalizability describing relationship between source components, an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis GBDT further validated above result. Undoubtedly, provided novel insights into utilizing ML predict through measurements excitation–emission matrix (EEM) specific Ex Em positions. could help us identify transformation species process, thus provide guidance optimized WWTPs.

Language: Английский

Citations

2
Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach DOI Open Access
Joanna Rosik, Sylwia Stegenta-Dąbrowska

Published: April 28, 2024

Although composting has many advantages in the treatment of organic waste, there are still problems and challenges associated with emissions, like NH3, VOCs, H2S, as well greenhouse gases such CO2, CH4, N2O. One promising approach to enhancing conditions is used novel analytical methods bad on artificial intelligence. To predict optimize emissions (CO, NH3) during process kinetics thought mathematical models (MM) machine learning (ML) were utilized. Data about everyday from laboratory compost’s biochar different incubation (50, 60, 70 °C) doses (0, 3, 6, 9, 12, 15% d.m.) for MM ML selections training. not been very effective predicting (R2 0.1 - 0.9), while acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0,71, CO2:0,81, NH3:0,95, H2S:0,72)) decision tree (DT, RPART; CO:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For first time CO H2S demonstrated. Further research a semi-scale field study needed improve developments models.

Language: Английский

Citations

2