The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of

p

-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1

]

. PX oxidation reaction involves many complex side reactions

among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process

the quality and yield of TPA are of great concern. However

the improvement of the qualified product yield can bring about the high energy consumption

which means that the economic objectives of this process cannot be achieved simultaneously because the two objectives are in conflict with each other. In this paper

an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization problems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems

and the performance measures such as convergence and divergence metrics are cal

culated. Subsequently

the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.