Simulation-Optimization Approaches to the Solve Strategic and Tactical Problems of Biomass Utilization for Energy and Commodity Chemicals

Simulation-Optimization Approaches to the Solve Strategic and Tactical Problems of Biomass Utilization for Energy and Commodity Chemicals

Ismail Fahmi, Selen Cremaschi

Department of Chemical Engineering, The University of Tulsa, 800 South Tucker Drive, Tulsa, Oklahoma 74104, USA

10C08 Poster Session: Computers In Operations and Information Processing

Abstract

As the fossil-based material reserves deplete, but its demand by energy and commodity chemicals are increasing, alternative feedstocks need to be discovered. One alternative feedstock is the renewable, locally available, and abundant biomass. Incorporating biomass as a feedstock to energy and commodity chemicals industry requires a significant amount of investments in capacity expansions and in the research and development (R&D). In addition, for each selected technology, the optimal configuration and operating conditions of unit operations should be identified. As a result, there are two investigation levels in the problem of incorporating biomass to both energy and commodity chemicals industries: (1) Strategic level, which covers all possible technologies supporting the overall biomass processing system and the optimal decision will answer in which technologies, when, and how much to invest, (2) Tactical level, which covers all production alternatives for a selected processing technology and the solution will identify the optimal process flowsheet and operating conditions. In this poster, two different simulation-optimization approaches are presented to address the strategic and tactical problems of incorporating biomass to energy and commodity chemicals production.

The first type of simulation-optimization approach, also known as the surrogate-based optimization, uses simulation to generate surrogate models to accurately represent the original first-order principal relationship. The surrogate models are then used in a deterministic optimization problem formulation, which is solved to yield the optimum solution. In this work, surrogate-based optimization is utilized to solve the process synthesis problems, which has been demonstrated in [1] with biodiesel production as the case study. The three process alternatives considered were using super critical methanol as reactant, using base catalyst, and using acid catalyst. In [1], it was shown that using surrogate models to replace the original first-order principal relationship of each and every unit operation in the optimization formulation can reduce the computational costs to obtain a solution.

The second type of simulation-optimization approach uses deterministic optimization with stochastic simulation in an integrated fashion to study the impact of uncertainties on the system performance. In the second simulation-optimization approach, deterministic optimization is used to produce the optimum decision set of the system at its current state. Stochastic simulation is used to predict the system performance under uncertainties. In a single simulation-optimization run, the simulation proceeds through the time steps of a timeline based on the decisions set determined by the deterministic optimization. Because of the accumulated differences between the expected and the realized behavior of the system, the system performance under these decisions may no longer be applicable or feasible at one point in time. This situation is called a trigger event. In case of a trigger event, the simulation is halted and deterministic optimization is recalled to produce a new set of optimal decision for the remainder of the timeline. The control is passed back to simulation along with the new set of decisions. The iteration between the deterministic optimization and stochastic simulation continues until the final time step. After the completion of the final step, a unique controlled evolution of the system performance under uncertainties (i.e., one unique timeline) is generated. Multiple unique timelines are produced because of different realizations of the uncertain parameters. The statistically significant number of timelines can be used to study the system performance under uncertainties. In this work, the second type of simulation-optimization approach is used to address the problem of strategic investment planning to shift from fossil-based feedstocks to biomass feedstock for the chemical process industry (CPI). This problem is referred to as the Biomass to Commodity Chemicals (BTCC) investment planning problem. A case study of ethylene production from biomass and naphtha has been presented to demonstrate the performance of the second type of the simulation-optimization [2]. However, generating a single timeline may require significant amounts of computational costs. The major sources of the computational costs are the number of timelines and the computational resources necessary to solve the deterministic optimization problem, which may be solved many times during a single timeline. To address the former problem, a systematic analysis on different sampling methods to cover the uncertain parameters space of the BTCC investment planning problem is presented. To address the latter problem, a study of the development of a specialized global solver to solve the optimization problem is presented.

Keywords: simulation-based optimization, biomass feedstock incorporation, strategic investment planning and tactical process synthesis problem

References:

[1]        I. Fahmi and S. Cremaschi, "Process Synthesis of Biodiesel Production Plant using Artificial Neural Networks as the Surrogate Models," Computers and Chemical Engineering (in review), 2011.

[2]        I. Fahmi and S. Cremaschi, "A Prototype Simulation-based Optimization Approach to Model Feedstock Development for Chemical Process Industry," 22nd European Symposium on Computer Aided Process Engineering, 2012.