Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning
W Zhang, R Chen, J Li, T Huang, B Wu, J Ma, Q Wen… - Biochar, 2023 - Springer
Due to large specific surface area, abundant functional groups and low cost, biochar is
widely used for pollutant removal. The adsorption performance of biochar is related to …
widely used for pollutant removal. The adsorption performance of biochar is related to …
Advancement and State-of-art of heterogeneous catalysis for selective CO2 hydrogenation to methanol
Addressing global warming while fulfilling the growing need for energy, fuels, and chemicals
is a huge challenge currently faced by civilization. Utilization of carbon dioxide (CO 2) or CO …
is a huge challenge currently faced by civilization. Utilization of carbon dioxide (CO 2) or CO …
Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland
This study aims to evaluate existing approaches for monitoring and assessing water quality
in waterbodies in the North of Ireland using newly developed methodologies. The results …
in waterbodies in the North of Ireland using newly developed methodologies. The results …
[HTML][HTML] Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model
The COVID-19 pandemic has significantly impacted various aspects of life, including
environmental conditions. Surface water quality (WQ) is one area affected by lockdowns …
environmental conditions. Surface water quality (WQ) is one area affected by lockdowns …
Design of Flame‐Made ZnZrOx Catalysts for Sustainable Methanol Synthesis from CO2
T Pinheiro Araújo, J Morales‐Vidal… - Advanced Energy …, 2023 - Wiley Online Library
Mixed zinc‐zirconium oxides, ZnZrOx, are highly selective and stable catalysts for CO2
hydrogenation to methanol, a pivotal energy vector. However, their activity remains …
hydrogenation to methanol, a pivotal energy vector. However, their activity remains …
[HTML][HTML] Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches
Assessing eutrophication in coastal and transitional waters is of utmost importance, yet
existing Trophic Status Index (TSI) models face challenges like multicollinearity, data …
existing Trophic Status Index (TSI) models face challenges like multicollinearity, data …
Artificial intelligence enabled efficient power generation and emissions reduction underpinning net-zero goal from the coal-based power plants
A large power generation facility is a complex multi-criteria system associated with
multivariate couplings, high dependency, and non-linearity among the operating variables …
multivariate couplings, high dependency, and non-linearity among the operating variables …
Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning
Y Yang, H Shahbeik, A Shafizadeh, S Rafiee, A Hafezi… - Energy, 2023 - Elsevier
The gasification process can treat and valorize municipal solid waste (MSW) in an
environmentally and economically friendly way. Using this process, MSW can be safely …
environmentally and economically friendly way. Using this process, MSW can be safely …
Machine learning based prediction and experimental validation of arsenite and arsenate sorption on biochars
Arsenic (As) contamination in water is a significant environmental concern with profound
implications for human health. Accurate prediction of the adsorption capacity of arsenite [As …
implications for human health. Accurate prediction of the adsorption capacity of arsenite [As …
[HTML][HTML] Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index …
Recently, there has been a significant advancement in the water quality index (WQI) models
utilizing data-driven approaches, especially those integrating machine learning and artificial …
utilizing data-driven approaches, especially those integrating machine learning and artificial …