4.6 Article

Simulation-Based Estimates of Life Cycle Inventory Gate-to-Gate Process Energy Use for 151 Organic Chemical Syntheses

Journal

ACS SUSTAINABLE CHEMISTRY & ENGINEERING
Volume 8, Issue 23, Pages 8519-8536

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acssuschemeng.0c00439

Keywords

Pinch analysis; Chemical process simulation; Chemical life cycle assessment; Chemical manufacturing energy; Heat integration

Funding

  1. U.S. National Science Foundation, under CAREER [CBET-1454414]

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Process energy data is integral to the sustainability analysis of chemical products and processes. However, due to the lack of primary data from chemical plants, process energy calculations in chemical life-cycle inventories (LCIs) often rely primarily on empirical averages, estimations based on proxy processes, or the industrial chemistry literature. In this study, we demonstrate a streamlined process simulation-based methodology to estimate energy consumption in chemical manufacturing when the data availability for the process is limited. The methodology is applied to 151 different chemical processes using Aspen Plus to estimate their gate-to-gate process energy use, representing the largest such simulation-based LCI data set to date. Further, pinch analysis used for process heat integration and specification of different utility types for each of the chemical processes enhance the representativeness of the LCI data. The total heating requirement for chemicals assessed ranges from 0.1 to 24 MJ/kg with an average of 3.1 MJ/kg product, while the average cooling requirement before heat integration is 4.5 MJ/kg. More than half of the total energy requirement comes from the separation section. Steam is the most used hot utility in the chemical industry which reflects in the simulation results, accounting for 70% of the heating requirements, while air and cooling water accounts for 40% of the cold utilities used. The engineering design-based estimates reported here represent a substantial addition to chemical LCI data and can provide a strong foundation for future predictive models for the large chemical universe.

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