Undoped ruthenium oxide as a stable catalyst for the acidic oxygen evolution reaction.

Reducing green hydrogen production cost is critical for its widespread application. Proton-exchange-membrane water electrolyzers are among the most promising technologies, and significant research has been focused on developing more active, durable, and cost-effective catalysts to replace expensive iridium in the anode. Ruthenium oxide is a leading alternative while its stability is inadequate.

Optimization of Operational Parameters Using Artificial Neural Network and Support Vector Machine for Bio-oil Extracted from Rice Husk.

Bio-oil production from rice husk, an abundant agricultural residue, has gained significant attention as a sustainable and renewable energy source. The current research aims to employ artificial neural network (ANN) and support vector machine (SVM) modeling techniques for the optimization of operating parameters for bio-oil extracted from rice husk ash (RHA) through pyrolysis. ANN and SVM methods are employed to model and optimize the operational conditions, including temperature, heating rate, and feedstock particle size, to enhance the yield and quality of bio-oil.

Screening of Metal Oxides and Hydroxides for Arsenic Removal from Water Using Molecular Dynamics Simulations.

Arsenic in groundwater is a harmful and hazardous substance that must be removed to protect human health and safety. Adsorption, particularly using metal oxides, is a cost-effective way to treat contaminated water. These metal oxides must be selected systematically to identify the best material and optimal operating conditions for the removal of arsenic from water.

Optimization of operational parameters using RSM, ANN, and SVM in membrane integrated with rotating biological contactor.

Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC.

SVM and ANN Modelling Approach for the Optimization of Membrane Permeability of a Membrane Rotating Biological Contactor for Wastewater Treatment.

Membrane fouling significantly hinders the widespread application of membrane technology. In the current study, a support vector machine (SVM) and artificial neural networks (ANN) modelling approach was adopted to optimize the membrane permeability in a novel membrane rotating biological contactor (MRBC). The MRBC utilizes the disk rotation mechanism to generate a shear rate at the membrane surface to scour off the foulants.

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment.

Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor.