Measuring the Resource Productivity of Crude Oil: A Chemical Network and its Application

Abstract

The resource productivity (RP) of crude oil is an important indicator to measure the utilization efficiency of a petroleum resource. In this article, we proposed a methodology to calculate the RP of crude oil from a complex network perspective. We constructed a chemical network comprising 578 chemicals and divided all chemicals into six hierarchical levels according to their processing steps. We put forward two indicators to represent two calculation methods: resource productivity based on material flow analysis and resource productivity based on carbon flow analysis (RPCF). To clarify the differences in the meanings of these two indicators, we extracted the para-xylene (PX) production chain, which is composed of crude oil, PX, pure terephthalic acid, and polyethylene terephthalate, from the network as an example. Finally, we adopted the RPCF indicator. We calculated that the average RPCFs of the six hierarchical levels of crude oil in China from 1992 to 1999 are 145, 219, 601, 929, 1,474, and 4,076 US$/(tonnes carbon). The results show that there is a value-added effect in the extension of petrochemical industrial chains. Among the derivatives of crude oil, the RPCFs of chemicals are obviously higher than those of oils. Countries, regions, industrial parks, and chemical companies can improve RP by extending and choosing chemical production chains and combinations thereof. We can also use the calculation methodology to compare RP of other sources of carbon like carbon dioxide and biomass with that of crude oil, and promote the development of circular economy in energy and chemical production. Keywords: chemical network China crude oil hierarchical levels industrial ecology resource productivity Supporting information is linked to this article on the JIE website Conflict of interest statement: The authors have no conflict to declare. Volume 22, Number 6growth. Jevons formally put forward the concept of RP for the first time, trying to solve the problem by improving the RP of coal. With the rise and wider acceptance of the sustainable development concept in the world, RP research has gradually attracted the attention of scholars. When the circular economy (CE) was mainstreamed into the national strategy, many regions and countries introduced RP as a key indicator to show

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application

“…Traditional research focuses on the element and energy flows in chemical enterprises or clusters, e.g., Ozalp 15 gave energy and material flow models of hydrogen production via steam reforming of methane in the United States, Chae et al 16 developed a mathematical model to synthesize the waste heat utilization network targeting a petrochemical complex, Tian et al analyzed the carbon metabolism, 17 sulfur metabolism 18 and energy metabolism 19 in a typical fine CIP, Guo et al 20 analyzed the carbon element flows in a natural gas CIP, and Ma et al 5 presented a general chlorine metabolic model at the industrial park level. Since the chemical flows involving multiple units and phases often form a complex network, programming algorithms are effective in related studies, including a genetic algorithm by Wu and Wang, 21 and improved machine learning models by data processing by Sun et al 22 Some studies focused on the network flow analysis algorithms to address the complexity of chemical industrial chains, e.g., Ren et al 23 proposed an algorithm methodology to calculate the resource productivity of crude oil from a complex network perspective.…”