(161c) Optimization for Sustainable Process Design

Eighty-four percent (84%) of the world’s energy supply comes from natural gas, oil, and coal [1], the use and extraction of which pose serious challenges to our environment. Today, the preeminent challenge is global warming. Global warming is a consequence of the massive discharge of greenhouse gases (mainly carbon dioxide) into the atmosphere during the burning of fossil fuels leading to continuous depletion of the environment [2]. This has led most countries to sign the Paris agreement, which aims to limit temperature increases to “well below 2°C above pre-industrial levels” by achieving net-zero emissions of greenhouse gases [2] around 2050.

One important way to support the global goal of net-zero emissions is the development of processes that reduce environmental impacts, including CO₂ emissions. This is the domain of process systems engineering (PSE). However, achieving this goal requires the development of PSE tools that focus not only on the optimization of technical and economic performance but the incorporation of environmental performance in process synthesis. Such optimization and decision support tools must be sufficiently robust that can support the exploration and analysis of various process alternatives under uncertainty and produce an optimal balance between profit maximization, energy efficiency, and environmental performance [3], [4]. This paper focuses on developing PSE tools that allow for the synthesis and optimization of processes that meet environmental design goals along with technical and economic performance.

We formulated a multiobjective optimization in MATLAB^® in which the objective functions are minimizing total cost and carbon footprint subject to certain constraints while the process alternatives are modelled as decision (binary) variables. Aspen Plus^® Software is utilized to model these alternative processes with accurate mass and energy balances. The results from the simulation were regressed to formulate mathematical models for the optimization.

A genetic algorithm solver is utilized to generate a Pareto front which shows the trade-off between carbon footprint and total cost, at the same time, giving the optimum reactor configuration at each local minimum. The multiobjective optimization using the genetic algorithm solver, a stochastic base approach, is compared with the gradient-based approach to compare the accuracy of the result and computational time.

These tools will be presented in the specific context of syngas production from municipal solid waste (MSW) via steam gasification. Four (4) different reactor configurations namely, plasma gasifier, entrained flow reactor, fluidized bed reactor, and fixed bed reactor to produced syngas were modelled and optimal configurations at best operating conditions were obtained with the constraint that the molar ratio of hydrogen to carbon monoxide (H2:CO) is suitable for the Fischer Tropsch process.

The results provide a set of optimal process alternatives and insight for decision-makers and companies to clearly decide depending on their priorities. Similarly, the outcome of this work will clearly demonstrate the application of superstructure optimization in chemical process design and its effectiveness in decision-making.

REFERENCE

[1] R. Rapier, ‘Fossil Fuels Still Supply 84 Percent Of World Energy — And Other Eye Openers From BP’s Annual Review’, Forbes. https://www.forbes.com/sites/rrapier/2020/06/20/bp-review-new-highs-in-g... (accessed Nov. 17, 2022).

[2] L. Xu, Y. Xiu, F. Liu, Y. Liang, and S. Wang, ‘Research Progress in Conversion of CO2 to Valuable Fuels’, Molecules, vol. 25, no. 16, p. 3653, Aug. 2020, doi: 10.3390/molecules25163653.

[3] I. E. Grossmann and G. Guillén-Gosálbez, ‘Scope for the application of mathematical programming techniques in the synthesis and planning of sustainable processes’, Computers & Chemical Engineering, vol. 34, no. 9, pp. 1365–1376, Sep. 2010, doi: 10.1016/j.compchemeng.2009.11.012.

[4] M. Martín and T. A. Adams II, ‘Challenges and future directions for process and product synthesis and design’, Computers & Chemical Engineering, vol. 128, pp. 421–436, Sep. 2019, doi: 10.1016/j.compchemeng.2019.06.022.