Advances in Modeling, Synthesis, and Global Optimization of Hybrid Energy Systems Toward the Production of Liquid Fuels and Olefins

Abstract

The pressing challenges within the energy and chemical industry are energy security, energy affordability, and the generation of lower carbon energy. The dependency on crude oil imports and the price volatility necessitate the addressing of these challenges through the use of alternative feedstocks such as biomass, municipal solid waste, natural gas, and shale gas. The aim is to produce liquid fuels and olefins to partially displace crude oil demand. These challenges can be systematically addressed within a multi-scale systems engineering framework, since each scale of concern exhibits different challenges. Then, each scale can be successfully bridged using mathematical techniques to ensure continuity. Hence, this work attempts to model, synthesize, and optimize innovative energy processes for the production of liquid fuels and olefins from alternative feedstocks.

First, reactor level challenges are addressed due to having (i) complex models, (ii) unknown kinetics, or (iii) dynamic reactor systems. Systematic modeling and parameter estimation methods are developed to represent mathematical models for energy processes such as municipal solid waste gasification, microchannel steam reforming, and steam cracking of natural gas liquids. These accurate and reduced order models enable the implementation into large-scale process superstructures, where competing alternatives can be evaluated within a single model.

The process superstructures, upon the conceptual design and rigorous modeling describe large-scale nonconvex MINLPs, which need to be solved to global optimality. A tailored global optimization algorithm is developed which guarantees global optimality within a few % of the best possible value. Then, energy and chemicals systems are investigated for (i) process intensification via liquids production, (ii) coproduction of fuels and chemicals, and (iii) stand-alone olefins production. The results suggest that these processes can be significantly profitable and the modeling framework can elucidate optimal conversion and upgrading methods. The model is further extended to handle price uncertainty via robust optimization, which reduced the standard deviation on the profit significantly. As a result, the multi-scale challenges for these processes are holistically addressed for a sustainable energy and chemical process synthesis.