[PDF][PDF] Implementation of machine learning methods for monitoring and predicting water quality parameters
G Hayder, I Kurniawan… - Biointerface Res. Appl …, 2020 - biointerfaceresearch.com
The importance of good water quality for human use and consumption can never be
underestimated, and its quality is determined through effective monitoring of the water …
underestimated, and its quality is determined through effective monitoring of the water …
Implementation of the neural networks for improving the projects' performance of steel structure projects
This paper aims at developing a model to measure and evaluate the performance and
productivity of the construction of steel structure projects (SSPs). Practitioners and experts …
productivity of the construction of steel structure projects (SSPs). Practitioners and experts …
Exploration of time series model for predictive evaluation of long-term performance of membrane distillation desalination
Owing to the inherent complications in membrane distillation (MD) operations, it has become
a challenge to acknowledge swiftly and appropriately to safeguard the quality of effluent …
a challenge to acknowledge swiftly and appropriately to safeguard the quality of effluent …
Membrane science meets machine learning: future and potential use in assisting membrane material design and fabrication
The evolving membrane technology integrated with machine learning (ML) algorithms can
significantly advance the novel membrane material design and fabrication. Although several …
significantly advance the novel membrane material design and fabrication. Although several …
Operational parameter prediction of electrocoagulation system in a rural decentralized water treatment plant by interpretable machine learning model
B Li, C Lu, J Zhao, J Tian, J Sun, C Hu - Journal of Environmental …, 2023 - Elsevier
Electrocoagulation (EC) is a promising alternative for decentralized drinking water treatment
in rural areas as a chemical-free technology. However, seasonal fluctuations of water quality …
in rural areas as a chemical-free technology. However, seasonal fluctuations of water quality …
Recent development in machine learning of polymer membranes for liquid separation
Emerged as a transformative technology, machine learning (ML) has demonstrated
unprecedented success in the design and discovery of new materials. Over the past few …
unprecedented success in the design and discovery of new materials. Over the past few …
Backwash sequence optimization of a pilot-scale ultrafiltration membrane system using data-driven modeling for parameter forecasting
Optimizing the backwashing procedure of ultrafiltration membranes poses novel challenges
in regards to the modeling and simulation of the fouling process. Traditional modeling …
in regards to the modeling and simulation of the fouling process. Traditional modeling …
[HTML][HTML] SPyCE: A structured and tailored series of Python courses for (bio) chemical engineers
In times of educational disruption, significant advances in adopting digitalization strategies
have been accelerated. In this transformation climate, engineers should be adequately …
have been accelerated. In this transformation climate, engineers should be adequately …
A machine learning and data analytics approach for predicting evacuation and identifying contributing factors during hazardous materials incidents on railways
An emergency evacuation order might be issued in response to a railway incident involving
hazardous materials (hazmat), such as the February 2023 derailment at Palestine, Ohio …
hazardous materials (hazmat), such as the February 2023 derailment at Palestine, Ohio …
[HTML][HTML] Machine learning toward improving the performance of membrane-based wastewater treatment: A review
Abstract Machine learning (ML) is a data-driven approach that can be applied to design,
analyze, predict, and optimize a process based on existing data. Recently, ML has found its …
analyze, predict, and optimize a process based on existing data. Recently, ML has found its …