A hybrid deep learning framework driven by data and reaction mechanism for predicting sustainable glycolic acid production performance DOI
Xin Zhou, Zhiyang Li, Xiang Feng

et al.

AIChE Journal, Journal Year: 2023, Volume and Issue: 69(7)

Published: March 8, 2023

Abstract Selective oxidation at low temperatures without alkali of biomass is a promising and sustainable avenue to manufacture glycolic acid (GA), biodegradable functional material protect the environment. However, producing with high selectivity yield using traditional research development approach time‐consuming labor‐intensive. To this context, hybrid deep learning framework driven by data reaction mechanisms for predicting GA production was proposed, considering lack related in machine algorithms. The proposed involves kinetic mechanism, catalyst properties, conditions. Results showed that fully connected residual network exhibited superior performance (average R 2 = 0.98) prediction conversion rate product yields. Then, multi‐objective optimization experimental verification guided are carried out. comparable modeling results, errors less than 4% life cycle assessment further identifies optimized operating parameters, fossil energy demand greenhouse emissions have decreased 2.96% 3.00%, respectively. This work provides new insight strategy accelerate engineered selective desired production.

Language: Английский

Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning DOI Creative Commons
Kumuduni Niroshika Palansooriya, Jie Li, Pavani Dulanja Dissanayake

et al.

Environmental Science & Technology, Journal Year: 2022, Volume and Issue: 56(7), P. 4187 - 4198

Published: March 15, 2022

Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. heavy metal (HM)-contaminated soil primarily depends on properties biochar, and HM. The optimum conditions HM immobilization in biochar-amended soils are site-specific vary among studies. Therefore, generalized approach to predict efficiency required. This study employs machine learning (ML) approaches biochar soils. nitrogen content (0.3–25.9%) rate (0.5–10%) were two most significant features affecting immobilization. Causal analysis showed that empirical categories efficiency, order importance, > experimental properties. this presents new insights into effects can help determine enhanced

Language: Английский

Citations

292
Recent advancements and challenges in emerging applications of biochar-based catalysts DOI Creative Commons
Xiangzhou Yuan, Yang Cao, Jie Li

et al.

Biotechnology Advances, Journal Year: 2023, Volume and Issue: 67, P. 108181 - 108181

Published: June 1, 2023

The sustainable utilization of biochar produced from biomass waste could substantially promote the development carbon neutrality and a circular economy. Due to their cost-effectiveness, multiple functionalities, tailorable porous structure, thermal stability, biochar-based catalysts play vital role in biorefineries environmental protection, contributing positive, planet-level impact. This review provides an overview emerging synthesis routes for multifunctional catalysts. It discusses recent advances biorefinery pollutant degradation air, soil, water, providing deeper more comprehensive information catalysts, such as physicochemical properties surface chemistry. catalytic performance deactivation mechanisms under different systems were critically reviewed, new insights into developing efficient practical large-scale use various applications. Machine learning (ML)-based predictions inverse design have addressed innovation with high-performance applications, ML efficiently predicts biochar, interprets underlying complicated relationships, guides synthesis. Finally, benefit economic feasibility assessments are proposed science-based guidelines industries policymakers. With concerted effort, upgrading protection reduce pollution, increase energy safety, achieve management, all which beneficial attaining several United Nations Sustainable Development Goals (UN SDGs) Environmental, Social Governance (ESG).

Language: Английский

Citations

101
Understanding and optimizing the gasification of biomass waste with machine learning DOI Creative Commons
Jie Li, Lanyu Li, Yen Wah Tong

et al.

Green Chemical Engineering, Journal Year: 2022, Volume and Issue: 4(1), P. 123 - 133

Published: May 27, 2022

Gasification is a sustainable approach for biomass waste treatment with simultaneous combustible H2-syngas production. However, this thermochemical process was quite complicated multi-phase products generated. The product distribution and composition also highly depend on the feedstock information gasification condition. At present, it still challenging to fully understand optimize process. In context, four data-driven machine learning (ML) methods were applied model prediction interpretation optimization. results indicated that Gradient Boosting Regression (GBR) showed good performance predicting three-phase syngas compositions test R2 of 0.82–0.96. GBR model-based suggested both feed condition (including contents ash, carbon, nitrogen, oxygen, temperature) important factors influencing char, tar, syngas. Furthermore, found higher carbon (> 48%), lower nitrogen (< 0.5%), ash (1%–5%) under temperature over 800 °C could achieve yield H2-rich It shown optimal conditions by an output containing 60%–62% H2 44.34 mol/kg. These valuable insights provided from aid understanding optimization guide production

Language: Английский

Citations

74
Prediction heavy metals accumulation risk in rice using machine learning and mapping pollution risk DOI
Bing Zhao, Wenxuan Zhu,

Shefeng Hao

et al.

Journal of Hazardous Materials, Journal Year: 2023, Volume and Issue: 448, P. 130879 - 130879

Published: Jan. 28, 2023

Language: Английский

Citations

64
Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion DOI
Yi Zhang,

Yijing Feng,

Zhonghao Ren

et al.

Bioresource Technology, Journal Year: 2023, Volume and Issue: 374, P. 128746 - 128746

Published: Feb. 20, 2023

Language: Английский

Citations

47
Application of machine learning in adsorption energy storage using metal organic frameworks: A review DOI

Nokubonga P. Makhanya,

Michael Kumi, Charles Mbohwa

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 111, P. 115363 - 115363

Published: Jan. 13, 2025

Language: Английский

Citations

2
Review of explainable machine learning for anaerobic digestion DOI
Rohit Gupta, Le Zhang,

Jiayi Hou

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128468 - 128468

Published: Dec. 9, 2022

Language: Английский

Citations

57
Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams DOI

To‐Hung Tsui,

Mark C.M. van Loosdrecht, Yanjun Dai

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128445 - 128445

Published: Dec. 5, 2022

Language: Английский

Citations

54
Machine learning framework for intelligent prediction of compost maturity towards automation of food waste composting system DOI
Xin Wan, Jie Li, Li Xie

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 365, P. 128107 - 128107

Published: Oct. 13, 2022

Language: Английский

Citations

39
Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification DOI
Ye Sun, Zhiyuan Zhao,

Hailong Tong

et al.

Environmental Science & Technology, Journal Year: 2023, Volume and Issue: 57(46), P. 17990 - 18000

Published: May 16, 2023

In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited best performances prediction reaction rate (k) based on training data set relevant to pollutant characteristics and conditions, indicated by Rext2 0.84 RMSEext 0.79. Based 315 points collected from literature, current density, concentration, gap energy (Egap) were identified be most impactful parameters available EO process. particular, adding conditions as input features allowed provision more information an increase in sample size improve accuracy. feature importance analysis was performed revealing pattern interpretation using Shapley additive explanations (SHAP). ML-based generalized random case tailoring optimum with phenol 2,4-dichlorophenol (2,4-DCP) serving pollutants. resulting predicted k values close experimental verification, accounting relative error lower than 5%. This study provides paradigm shift conventional trial-and-error mode data-driven advancing research development time-saving, labor-effective, environmentally friendly strategy, which makes purification efficient, economic, sustainable context global carbon peaking neutrality.

Language: Английский

Citations

34