This work gives an outline of the methods and tools needed to model complex chemical systems.
The Nobel Prize for Chemistry went to Martin Karplus of Harvard University, Michael Levitt of Stanford University and Arieh Warshel of USC for their work using computers to model complex chemical processes.
Experimental and theoretical aspects of chemical reaction kinetics, including transition-state theories, molecular beam scattering, classical techniques, quantum and statistical mechanical estimation of rate constants, pressure-dependence and chemical activation, modeling complex reacting mixtures, and uncertainty/ sensitivity analyses.
In order to evaluate the performance of the proposed regression model, case studies of modeling two complex chemical processes are provided.
In recent years, the concept of neural networks (NNs) has gained wide popularity in many areas of chemical engineering such as dynamic modeling of chemical processes [18, 19], catalyst design [20], estimation of catalyst deactivation, reaction modeling [21, 22], modeling of chemical reactors [23] and modeling of complex chemical processes [24].
Such a limited context would be insufficient for modelling the complex chemical interactions within materials.
Quantitative structure activity relations (QSAR) attempt to model complex nonlinear relationships between the chemical and physical properties of molecules and their biological activity (35, 36).
It is possible to model complex community dynamics in relation to the chemical and physical dynamics of the ecosystem, even without exhaustive sequence and environmental data.
The kinetic data were well fitted to the pseudo-second-order model, and the results of intraparticle diffusion model showed complex chemical reaction might be involved during adsorption process.
