Development trajectory of the attainable region optimization method: Trends and opportunities for applications in the waste-to-energy field

Abstract Most chemical and biochemical processes that yield desired products comprise of chemical reactions with inherent mass and heat transfer effects. Hence, it is desirable to optimize the effect of different input variables such as mixing rates, heating rates, flowrates, pressure and concentration of raw materials in order to ensure a high resource efficiency. The Attainable Region (AR) is an optimization method that uses geometric techniques to represent a region of all possible outcomes derived from a combination of parameters, bound by lines showing the limitations of the system. This review explores the successful development of AR in solving chemical reaction and processing problems, against the backdrop of limitations portrayed by other optimization techniques. The research demonstrates that AR methods have found a niche in this field, with many successful applications to problems of industrial importance such as in ammonia, methanol, butanol, lignocellulosic ethanol and comminution. This is due to the ability of AR to map out a global solution, unlike mathematical optimization superstructures which can give local optima, leaving the proprietor wondering if a better alternative does not exist. Coupled with a clear illustrative graphic interpretation and versatility in application, AR has a large potential impact in process optimization. There is no better time for such a tool, than the emergence of an environmentally conscious and resource efficient generation. Consequently, there is an increased demand for higher production efficiencies for both fossil and renewable energy feedstocks to curb emissions and reduce production costs, respectively. This research also traces AR applications in processes relevant in Waste-to-Energy (WtE) - a modern day research field where reactive conversions such as pyrolysis, gasification and bio-digestion are core. The complexities and catalytic inhibitions characterizing WtE conversions, heterogeneity of feedstock and poorly understood kinetics and thermodynamics of the reactions are actually a good opportunity for an optimization tool such as AR. Since AR can be built from experimental data or theoretical kinetic or thermodynamic models, it is a good choice optimization technique to employ in such a field with so many research questions and feedstock regimes.