Machine learning has attracted extensive interest in the process engineering field, due to the
capability of modeling complex nonlinear process behavior. This work presents a method for …

Methodology and application of artificial neural networks in structure–activity relationships
are reviewed focusing on the most frequently used three‐layer feedforward back …

Kinetic model identification relies on accurate concentration measurements and physical
constraints to limit solution multiplicity. Not having these measurements and prior knowledge …

Microkinetic modelling facilitates a detailed description of chemical reactions and has been
an important tool in heterogeneous catalysis research to resolve mechanisms at the …

In this paper, we are going to report results of a study which has been performed to
investigate inhibition of cytochrome P450 (CYP) by ciprofloxacin (CIF) in which …

New aspects of neural modelling of chemical reactors have been investigated in this study.
An universal method to create a family of neural models, useful for the reactor and reacting …

Leaching is a complex solid–liquid reaction which has an important influence on the
recovery efficiency of the spent lithium-ion batteries (LIBs). Therefore, it is of significant …

Carbon dioxide emission is well recognized as the main source of global warming. The
catalytic hydrogenation of carbon dioxide to methanol represents an effective method for …

Abstract Machine learning (ML) is used to provide reactions rates appropriate for models of
low temperature plasmas with a focus on A+ B $\rightarrow $ C+ D binary chemical …

The kinetic study of valuable metals recovery from waste lithium-ion batteries (LIBs) using
the artificial neural network (ANN) was investigated. A multilayer feed-forward artificial …