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Nanocomposite film performance parameters, including barrier and mechanical properties for packaging films, can be affected by variables such as the type and the concentration of nanoparticles. In food packaging, it is desired to develop optimal films to maintain the freshness of food for longer periods. In this investigation, polyvinyl alcohol (PVA) nanocomposite films were prepared by solution casting method with different combinations of Montmorillonite (MMT) platelets and titanium oxide (TiO2) spherical nanoparticles. A support vector machine (SVM) was implemented to study the thin nanocomposite films' behavior to changes in the independent variables. The SVM model predicted oxygen transmission rate (OTR), water vapor permeability (WVP|), Young ̓s Modulus (YM), total color difference (ΔE), opacity, tensile strength (TS), and elongation at the breakpoint (EB) with an error of less than 6.4%. A Genetic Algorithm (GA) was applied to find the optimal nanoparticle concentration to achieve the optimum film performance. Results have clearly shown that the optimum film performance depends on the type and the concentration of nanoparticles. In this investigation, results showed that the optimum loading of nanoparticles should be between 0.5 and 1.0 wt% for TiO2 and 2.5–3.5 wt% for MMT. [ABSTRACT FROM AUTHOR]

Due to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives. [ABSTRACT FROM AUTHOR]