(15a) Plant-Wide Scheduling for Profitable Emission Reduction in Petroleum Refineries | AIChE

(15a) Plant-Wide Scheduling for Profitable Emission Reduction in Petroleum Refineries


Xu, J. - Presenter, Lamar University
Zhang, J., Lamar University
Wang, S., Lamar University
Xu, Q., Lamar University
Scheduling of front-end crude supply and refinery operations is an important part of petroleum supply-chain management. And in reality, emissions from refineries are significant and emission reduction is a very challenging task. It involves a large-scale complex manufacturing system and thousands of process streams. Refineries in one hand are eager to improve production solutions to leverage profitability margins in nowadays volatile market; on the other hand, they have to pursue cost-effective pollution prevention technologies to comply with stricter environmental regulations, such as air emission requirements.

Cost-effective solution strategies require emission reductions to be addressed from the entire plant point of view, where emission source generations and possible utilizations should be well balanced. But it is a very challenging task due to the large-scale complex manufacturing system, which consists of over 17 major facilities with more than 85 main operation units, and thousands of process streams. And it should focus on the comprehensively study of material, energy, and information exchanges within the entire manufacturing system, so as to identify the best solutions to the entire plant. Mendez et al. presented a simultaneous optimization approach for blending and scheduling of refinery plant.[1] The model could be either discrete or continuous formulation. Meanwhile, Pinto et al. presented a general modeling framework for petroleum supply chain optimization.[2, 3] But their work did not sufficiently characterize stream properties that are major concerns in refinery productions.

In this paper, a new methodology framework together with a new general scheduling model have been developed for emission conscious crude unloading, transferring, and processing (ECUTP) operations to achieve profitable emission-reduction scheduling (PERS) from the entire system point of view. To identify PERS strategies for refinery plants, a production scheduling model covers both front-end and refinery has to be involved. The key is to optimize the overall material and energy flows to make the plant net profit maximum, meanwhile to ensure emission source generations and utilizations to be smartly balanced. Other manufacturing constraints, such as operation specifications and inventory limits, should also be satisfied as well. Major air emissions from refineries: such as CO2, volatile organic compounds (VOC), nitrogen oxides (NOX), and particulate matters (PM) are characterized and quantified. Then, comprehensive analysis based on the scheduling results from the model will be conducted. Since the plant profit and emission can be quantitatively evaluated, alternative plant design and operations for PER opportunities might be raised, which may come from fundamental/theoretical analysis or industrial expertise. Based on that, the scheduling model will be modified and solved again to examine whether the proposed PER opportunity is feasible. If it is not virtually accomplished, the proposed new design or operations should be refined. Through such a model improvement iteratively, a feasible PER design or operation strategy will finally be generated.

The scheduling model is a large-scale mixed integer nonlinear programming problem (MINLP). The efficacy of the proposed emission conscious scheduling model has been demonstrated by different case studies. PERS thrusts target on emission source reductions in a profitable and systematic way, which are economically attractive, environmentally benign, and technologically viable for petroleum refineries. It may also help emission generators (not just limited to petroleum refineries) to proactively and systematically reduce their emissions to meet increasingly strict economic and environmental challenges.


  1. Méndez, C.A., et al., A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Computers & Chemical Engineering, 2006. 30(4): p. 614-634.
  2. Pinto, J.M., M. Joly, and L.F.L. Moro, Planning and scheduling models for refinery operations. Computers & Chemical Engineering, 2000. 24(9): p. 2259-2276.
  3. Neiro, S.M.S. and J.M. Pinto, A general modeling framework for the operational planning of petroleum supply chains. Computers & Chemical Engineering, 2004. 28(6): p. 871-896.


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