Multi-objective optimisation of a double contact double absorption sulphuric acid plant for cleaner operation

Multi-objective optimisation of a double contact double absorption sulphuric acid plant for... The release of oxides of sulphur (SOx) and acid mist (H2SO4) during the production of sulphuric acid in the double contact double absorption (DCDA) process is hazardous to the environment. It is a challenging task to minimise these emissions while keeping plant operation within the production requirements and maximise revenue. In this study, SOx emissions, acid mist emissions, and net revenue are considered as objectives for multi-objective optimisation (MOO) of the DCDA process. Firstly, the process is modelled and simulated in Aspen HYSYS, and validated with plant data. MOO is then performed using the elitist non-dominated sorting genetic algorithm to predict sets of Pareto-optimal operating conditions for improved environmental and economic performance. The effect of operating parameters such as air flow rate and pressure, inlet temperatures to catalytic beds and absorbers, demineralized water flow rate, and boiler feed water flow rate on the process performance is also studied. The results show that the DCDA process can be operated at different optimal conditions, each of which involves some trade-off among the objectives of interest. A multi-criteria decision-making technique (known as technique for order of preference by similarity to ideal solution, TOPSIS) is used to determine the most suitable optimum operating point. Among the optimal conditions, the chosen solution through TOPSIS has 9.5 ppm of SOx emissions, 70.9 ppm of acid mist emission and 143.0 M$/y of net revenue (i.e., gross profit). The air flow rate strongly influences the objectives in opposite direction; at the selected optimum solution, it provides improved environmental and economic performance within acceptable limits of product quality. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cleaner Production Elsevier

Multi-objective optimisation of a double contact double absorption sulphuric acid plant for cleaner operation

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0959-6526
D.O.I.
10.1016/j.jclepro.2018.01.239
Publisher site
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Abstract

The release of oxides of sulphur (SOx) and acid mist (H2SO4) during the production of sulphuric acid in the double contact double absorption (DCDA) process is hazardous to the environment. It is a challenging task to minimise these emissions while keeping plant operation within the production requirements and maximise revenue. In this study, SOx emissions, acid mist emissions, and net revenue are considered as objectives for multi-objective optimisation (MOO) of the DCDA process. Firstly, the process is modelled and simulated in Aspen HYSYS, and validated with plant data. MOO is then performed using the elitist non-dominated sorting genetic algorithm to predict sets of Pareto-optimal operating conditions for improved environmental and economic performance. The effect of operating parameters such as air flow rate and pressure, inlet temperatures to catalytic beds and absorbers, demineralized water flow rate, and boiler feed water flow rate on the process performance is also studied. The results show that the DCDA process can be operated at different optimal conditions, each of which involves some trade-off among the objectives of interest. A multi-criteria decision-making technique (known as technique for order of preference by similarity to ideal solution, TOPSIS) is used to determine the most suitable optimum operating point. Among the optimal conditions, the chosen solution through TOPSIS has 9.5 ppm of SOx emissions, 70.9 ppm of acid mist emission and 143.0 M$/y of net revenue (i.e., gross profit). The air flow rate strongly influences the objectives in opposite direction; at the selected optimum solution, it provides improved environmental and economic performance within acceptable limits of product quality.

Journal

Journal of Cleaner ProductionElsevier

Published: Apr 20, 2018

References

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