(372q) The Use of Process Graph for the Design of Pyrolysis Oil Biorefinery Superstructure, and Optimization in Terms of Cost
Pyrolysis oil is a liquid biofuel produced by fast pyrolysis of biomass, a thermochemical conversion at relatively high temperature, in an oxygen-free atmosphere. The oil formed is rich in oxygenate compounds and water. In order to be used as a transportation fuel, pyrolysis oil needs to be upgraded to a mixture of hydrocarbons. The main technology currently studied for pyrolysis oil refine is the two-step hydrotreatment method. However, although this technique does produce hydrocarbon fuels, it comes with some drawbacks such as high hydrogen consumption, fuels rich in aromatics, and high cost. Nevertheless, the rich composition of pyrolysis oil (over 300 components) offers many opportunities to produce chemicals and materials, that could be co-products along with hydrocarbon fuels, or the main products. In fact, there are several operating units available in process engineering for separation of materials, such as distillation, extraction, membrane separation, among others. It is becoming clear that the economical feasibility of pyrolysis oil utilization will depend on production of value products, and various authors have been working in this direction. Still, no systematic view of the whole process is available, which would help the selection of the best integration strategies. The use of process graph (p-graph) method is suggested in this work to fill this gap. This methodology for Process Network Synthesis (PNS) was developed in the early 90âs by Friedler et al (1992), it is a very user-friendly tool, and it is design to avoid systematic and semantic errors. Shortly, there are three effective algorithms in p-graph: one to generate the maximal structure (MSG), one to generate the solution structures (SSG), and another one to generate the optimal structure (ABB). Therefore, with the use of this relatively simple tool, one could represent a maximal structure for a pyrolysis oil biorefinery, along with all possible scenarios (different pathways to form different products), and optimum network in terms of a specific property, such as cost. For this work, we used a case of study to prove the use of p-graph with the purpose of generating and selecting an optimum biorefinery network. Our case of study is based on the paper of Vitasari et al (2015): âConceptual process design of an integrated bio-based acetic acid, glycolaldehyde, and acetol production in a pyrolysis oil-based biorefineryâ. In that paper, the authors used Aspen Plus and Aspen Process Economic Analyzer softwares to study the process design of an integrated biorefinery. Our work aims to show that p-graph as a powerful, and simpler tool for PNS. As a result of our work, we will present a superstructure of the integrated biorefinery, the number of possible solutions and the cost associated to the production of each product, and the optimum network in terms of cost, i.e., which is the best product in terms of lowest cost associated.