Artificial Neural Approach for Modeling the Heat and Mass Transfer Characteristics in Three-Phase Fluidized Beds

Abstract

The study of reactor design and modeling is conducted frequently both at the initial stage of equipment design as well as during further stages of equipment operation. Fluidized bed three-phase reactors have very complex behavior which relies to a high extent on the mass and heat transfer characteristics of the reaction constituents. Numerous previous experimental and theoretical based studies for modeling heat and mass transfer coefficients have the common shortcoming of low prediction efficiency compared to experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The newly developed heat and mass transfer coefficients models proved to be of high prediction quality compared to experimental data and previous correlations. The new correlations will be used in a further study for the hybrid steady state and dynamic modeling of fluidized bed catalytic reactors.

Artificial Intelligence-based Modeling of High Ash Coal Gasification in a Pilot Plant Scale Fluidized Bed Gasifier

“…The AI-based modeling strategies, for example, artificial neural networks (ANNs), and the statistical machine learning (ML) theory-based formalism, namely support vector regression (SVR), are exclusively data-driven strategies and thus can be used for modeling the FBCG. There exist a number of studies wherein ANNs have been employed in the energy-related science and engineering. − In an exhaustive FBCG data-driven modeling study, Chavan et al developed two ANN-based models for the prediction of gas production rate and heating value of the product gas, using the process data from the 18 globally located coal gasifiers. These models use six inputs namely, fixed carbon, volatile matter, mineral matter, air feed per kilogram of coal, steam feed per kilogram of coal, and temperature.…”