(629e) Analysis of Different Sampling Methods for the Simulation-Based Optimization Approach to Model Feedstock Development for Chemical Process Industry

Analysis of Different Sampling
Methods for the Simulation-based Optimization Approach to Model Feedstock
Development for Chemical Process Industry

Ismail Fahmi, Selen Cremaschi

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

10C04: Energy and Sustainability in Operations

Abstract

Chemical Process Industry (CPI)
has been highly dependent on the fossil-based materials as the feedstock. As
the reserve for this non-renewable resource depletes, alternative resources
need to be discovered. Among many options, biomass is considered very promising
because it is renewable, locally available, and abundant. However,
incorporating biomass as a feedstock for CPI requires significant amounts of
investments for both research and development (R&D) and production capacity
expansions. How these investments will shape the evolution of the biomass to
commodity chemicals (BTCC) system should be investigated. Moreover, the
uncertain decision-dependent endogenous technology evolutions of the BTCC
system complicate the analysis. Fahmi and Cremaschi [1] have presented a prototype simulation-based
optimization (SIMOPT) approach to study the feedstock development for CPI. The
SIMOPT framework explained in [1] uses the concept of generating multiple
unique timelines to represent various controlled evolution of the system under
uncertainties. The SIMOPT framework [1] uses deterministic optimization with
stochastic simulation in an integrated fashion to study the impact of
uncertainties on the system performance. Deterministic optimization is used to
produce the optimum decision set for the system at its current state. The
optimization formulation for the BTCC investment planning used in the SIMOPT
framework is explained in detail in [2]. Stochastic simulation is used to predict
the system performance under uncertainties. In a single SIMOPT run, the
simulation proceeds through the time steps based on the decision 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 the original decisions may no longer be applicable or
feasible at one point in time. This condition is called a trigger event. In
case of a trigger event, the simulation is halted and deterministic
optimization is recalled to determine a new set of optimal decisions 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. To have a thorough analysis of the BTCC system
performance accounting for different uncertainties, a statistically significant
number of timelines are required, i.e. the quality of the results depends on
the quality of the uncertain variables' space coverage. This can simply be done
by gathering a virtually infinite number of timelines. However, such an approach
would be prohibitively expensive in computational costs. Therefore, the minimum
number of timelines that will achieve preset statistical significance in the
results should be determined. In this work, a systematic analysis using different
sampling methods to cover the uncertain parameters space of the BTCC investment
planning problem is presented. The sampling methods considered in this work are
Monte Carlo Sampling, Latin Hypercube Sampling, Univariate Dimension Reduction,
Hammersley Sampling, and Halton Sampling. The performance metrics used for the
comparison of these methods are the stabilization of four statistical moments (i.e.,
mean, variance, skewness, and kurtosis) of the objective and the overall objective
distribution shape using the Smirnov test. A BTCC system of ethylene production
is presented as the case study.

Keywords: BTCC investment planning, simulation-based
optimization, sampling methods comparison

References:

[1]        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.

[2]        I. Fahmi and S. Cremaschi,
"Stage-gate Representation of Feedstock Development for Chemical Process
Industry," Foundations of Computer-Aided Process Operations 2012, vol.
, 2011.