Abstract

Two process designs for the separation section of a flexible dimethyl ether and methanol polygeneration plant are presented, as well as an optimization method which can determine the optimal design under market uncertainty quickly and to global optimality without loss of model fidelity. The polygeneration plant produces a product mixture that is either mostly dimethyl ether or mostly methanol depending on market conditions by using a classic two-stage dimethyl ether production catalytic reaction route in which the second stage is bypassed when the market demand is such that methanol production is more favourable than dimethyl ether. The downstream distillation sequence is designed to purify the products to desired specifications despite the wide variability in feed condition that corresponds to the upstream reaction system operating either in DME-rich or methanol-rich mode. Because the optimal design depends on uncertain market conditions (realized as the percentage of the time in which the plant operates in either DME-rich or methanol-rich mode), this uncertainty is considered in the formulation of the optimal design problem. The results show that using one set of flexible distillation columns for two different objectives is superior to the “traditional” approach of using two different sets of distillation columns which are each optimized for one specific operating condition. Different approaches to design under uncertainty were considered, with a scenario-based two-stage stochastic formulation with a uniform distribution of the uncertain parameter recommended as the preferred formulation.

Highlights

  • As demonstrated in section Flexible Polygeneration Optimization Formulation and Solution, the optimal design is a strong function of the expected amount of time spent in Maximize DME Mode over the course of its lifetime

  • 99 Flexible Case B designs were made by solving Problem 3 on the range 0 < φExp,D < 1 in steps of 0.01. For each of those designs, the actual total annualized cost (TAC) (TACCaseB,Act) was computed as a function of φAct,D, which is the actual percentage of time spent in Maximize DME mode experienced by the plant once constructed, as follows: TACCaseB,Act = af TDCB1 + af TDC21 + AOCB1, Act + AOCB2, Act

  • This study presented two flexible versions of a distillation process designed to handle large changes in feed composition in order to produce different chemical products based on market demand

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Summary

INTRODUCTION

The term polygeneration refers to chemical plants that generate more than one kind of product. We explore flexible distillation design configurations for two feed conditions (parameter realizations), and thereafter investigate a scenario-based two-stage stochastic formulation for consideration of uncertainty in the total length of time that the distillation systems process the specific feed types This uncertainty parameter is used to describe all unknown factors that would impact the operating decision to produce one product versus another at any given time, including market conditions and other external factors. As shown, the optimal column diameters, stage counts, and heat exchanger sizes can be very different when comparing the same column under the different feed conditions experienced in the Methanol or DME modes It may be better in some cases to design a different flexible design with two columns which instead of having one column dedicated to DME product recovery and the other dedicated to Methanol and water recovery, one column is dedicated to the larger job and one is dedicated to the smaller job. In section Design Under Uncertainty, we have chosen to use Flexible Case B for the design under uncertainty analysis even though it is slightly more expensive than Flexible Case A because Case B has more degrees of freedom and requires more computational power to solve, and better demonstrates that optimal design under uncertainty problems can be solved to guaranteed global optimality using our approach within reasonable amounts of time

DESIGN UNDER UNCERTAINTY
Findings
CONCLUSIONS
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