Artificial neural network and its applications: Unraveling the efficiency for hydrogen production

Abstract Data models are designed for logically structuring a database. It defines how data are interconnected and how they can be processed in a system. Predictive models commonly use statistical techniques that rely on data and statistics to predict data outcomes with data models. Artificial neural network is based on computational algorithm replicating the biological network of human brains composed of neurons, used for solving complex nonlinear functions. The general form of this model is referred to as a “black box” model representing high dimensional, nonlinear data. The ANN model has found extensive application in various fields like medical science, environmental engineering, weather forecasting, and economics. Hydrogen production from various biomass represents a new alternative to the conventional energy sources. Hence, modeling and subsequent optimization provide better insight for hydrogen production. Present study aims to summarize the use of ANN in various fields with special emphasis on hydrogen production.