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This paper describes an innovative machine learning (ML) model to predict the performance of different metal oxide photocatalysts on a wide range of contaminants. The molecular structures of metal oxide photocatalysts are encoded with a crystal graph convolution neural network (CGCNN). The structure of organic compounds is encoded via digital molecular fingerprints (MF). The encoded features of the photocatalysts and contaminants are input to an artificial neural network (ANN), named as CGCNN-MF-ANN model. The CGCNN-MF-ANN model has achieved a very good prediction of the photocatalytic degradation rate constants by different photocatalysts over a wide range of organic contaminants. The effects of the data training strategy on the ML model performance are compared. The effects of different factors on photocatalytic degradation performance are further evaluated by feature importance analyses. Examples are illustrated on the use of this novel ML model for optimal photocatalyst selection and for assessing other types of photocatalysts for different environmental applications. [ABSTRACT FROM AUTHOR]

Nowadays, biochar is being studied to a great degree because of its potential for carbon sequestration, soil improvement, climate change mitigation, catalysis, wastewater treatment, energy storage, and waste management. The present review emphasizes on the utilization of biochar and biochar-based nanocomposites to play a key role in decontaminating dyes from wastewater. Numerous trials are underway to synthesize functionalized, surface engineered biochar-based nanocomposites that can sufficiently remove dye-contaminated wastewater. The removal of dyes from wastewater via natural and modified biochar follows numerous mechanisms such as precipitation, surface complexation, ion exchange, cation–π interactions, and electrostatic attraction. Further, biochar production and modification promote good adsorption capacity for dye removal owing to the properties tailored from the production stage and linked with specific adsorption mechanisms such as hydrophobic and electrostatic interactions. Meanwhile, a framework for artificial neural networking and machine learning to model the dye removal efficiency of biochar from wastewater is proposed even though such studies are still in their infancy stage. The present review article recommends that smart technologies for modelling and forecasting the potential of such modification of biochar should be included for their proper applications. [ABSTRACT FROM AUTHOR]

In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered. [ABSTRACT FROM AUTHOR]