Optimal Production Planning and Pollution Control in Petroleum Refineries Using Mathematical Programming and Dispersion Models

Munawar A Shaik,Ali Elkamel,Saad A Al-Sobhi,Amani Alnahdi,Fatih S Erenay

doi:10.3390/su11143771

Abstract

Oil refineries, producing a large variety of products, are considered as one of the main sources of air contaminants such as sulfur oxides (SOx), hydrocarbons, nitrogen oxides (NOx), and carbon dioxide (CO2), which are primarily caused by fuel combustion. Gases emanated from the combustion of fuel in an oil refinery need to be reduced, as it poses an environmental hazard. Several strategies can be applied in order to mitigate emissions and meet environmental regulations. This study proposes a mathematical programming model to derive the optimal pollution control strategies for an oil refinery, considering various reduction options for multiple pollutants. The objective of this study is to help decision makers select the most economic pollution control strategy, while satisfying given emission reduction targets. The proposed model is tested on an industrial scale oil refinery sited in North Toronto, Ontario, Canada considering emissions of NOx, SOx, and CO2. In this analysis, the dispersion of these air pollutants is captured using a screening model (SCREEN3) and a non-steady state CALPUFF model based on topographical and meteorological conditions. This way, the impacts of geographic location on the concentration of pollutant emissions were examined in a realistic way. The numerical experiments showed that the optimal production and pollution control plans derived from the proposed optimization model can reduce NOx, SOx, and CO2 emission by up to 60% in exchange of up to 10.7% increase in cost. The results from the dispersion models verified that these optimal production and pollution control plans may achieve a significant reduction in pollutant emission in a large geographic area around the refinery site.

Highlights

Global warming and the associated risks have been debated in recent decades, and climate change has been raised as a global ecological concern [1]
The focus is on reducing one emission at a time without considering the other polluItnantthsi,sinscoerndaerrioto, tahneafloyczuesthise oimn preadctuocifnrgedounceinegmeiasscihonematisasitoinmiendweiptheonudtecnotlnysiadnedritnogfitghueroetohuetr tphoellouvtearnatlsl, rienfionredreyr ctoosatncaolryrzeespthoendiminpgalcyt. oCformedpuacriinsognesacohf ceomstisisniocrneminednetpwenhdeennrtleydauncdintgoSfOigxu,rNe Oouxt, athned oCvOer2aelml riesfsiinoenrsyacroesgt icvoernreisnpToanbdleinsg3l–y5.,Creosmpepcatriivseolny.s of cost increment when reducing sulfur oxides (SOx), nitrogen oxides (NOx), and CO2 emissions are given in Tables 3–5, respectively
This study addresses the problem of selecting the best pollution control strategies for an oil refinery given specific values of emission reduction targets

Summary

Introduction

Global warming and the associated risks have been debated in recent decades, and climate change has been raised as a global ecological concern [1]. Oil refineries are one of the significant sources of air contaminants including, sulfur oxides (SOx), hydrocarbons, nitrogen oxides (NOx), particulate matter, volatile organic compounds, and carbon dioxide (CO2) [1] Reducing gases such as CO2, NOx, and SOx, released from burning fuel to supply heat to different units in oil refineries, is a priority for the welfare of the society. Strategies for increasing production from sources associated with less emission may be developed, e.g., one option to reduce CO2 emissions is switching to non-fossil fuels (e.g., biofuels) Another possible approach could be load shifting, which considers retrofitting production throughput across the refinery units for the sake of emissions reduction [5]. The Canadian Regulatory Analysis Guide outlines a general methodology and an analytical hierarchy to carry out cost-benefit analyses of oil refineries using different strategies for emission reduction of pollutants, and provides a case study to demonstrate identification of additional choices [7]

Objectives

Results

Conclusion