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Optimal Operation for 3 Control Parameters
of Texaco Coal-Water Slurry Gasifier
with MO-3LM-CDE Algorithms
Cuiwen Cao, Yakun Zhang, and Xingsheng Gu
Key Laboratory of Advanced Control and Optimization for Chemical Processes,
Ministry of Education, East China University of Science and Technology,
200237 Shanghai, P.R. China
{caocuiwen,xsgu}@ecust.edu.cn
Abstract.
Optimizing operation parameters for Texaco coal-water slurry
gasifier with the consideration of multiple objectives is a complicated nonlinear
constrained problem concerning 3 BP neural networks. In this paper, multi-
objective 3-layer mixed cultural differential evolution (MO-3LM-CDE)
algorithms which comprise of 4 multi-objective strategies and a 3LM-CDE
algorithm are firstly presented. Then they are tested in 6 benchmark functions.
Finally, the MO-3LM-CDE algorithms are applied to optimize 3 control
parameters of the Texaco coal-water slurry gasifier in methanol production of a
real-world chemical plant. The simulation results show that multi-objective
optimal results are better than the respective single-objective optimal
operations.
Keywords:
Operation parameters, gasifier; multi-objective, MO-3LM-CDE.
1
Introduction
Gasification is a vital component of “clean coal” technology. [1] Texaco coal-water
slurry gasifier is one of the most important upstream units in gasification process, and
it generates synthesis gas which can be used to produce a wide range of chemical
products. Optimal operation with the consideration of multiple objectives for the
control parameters of gasifier can greatly increase the production efficiency and
economic benefits.
In recent years, evolutionary algorithms (EAs) have been introduced to solve
multi-objective optimization problem. Among them, differential evolution (DE)
algorithm is one of the most efficient algorithms for problems over continuous space.
It has been widely used to solve many optimization problems [2]-[4]. Enlightened by
the far more rapid evolving velocity of human society, Reynolds [5] developed
cultural algorithm (CA). CA is a dual inheritance system that models evolution at
both the population space and the upper belief space. Quite a few EAs, such as DE
[6]-[8], have been adopted in population space instead of evolutionary programming
in standard CA, and multi-objective differential evolution algorithms (MODEs)
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