In this work, a modeling technique utilizing the P-Graph framework was used for a case study involving biomass-based local energy production. In recent years, distributed energy systems gained attention. These systems aim to satisfy energy supply demands, support the local economy, decrease transportation needs and dependence on imports, and, in general, obtain a more sustainable energy production process. Designing such systems is a challenge, for which novel optimization approaches were developed to help decision making. Previous work used the P-Graph framework to optimize energy production in a small rural area, involving manure, intercrops, grass, and corn silage as inputs and fermenters. Biogas is produced in fermenters, and Combined Heat and Power (CHP) plants provide heat and electricity. A more recent result introduced the concept of operations with flexible inputs in the P-Graph framework. In this work, the concept of flexible inputs was applied to model fermenters in the original case study. A new implementation of the original decision problem was made both as a Mixed-Integer Linear Programming (MILP) model and as a purely P-Graph model by using the flexible input technique. Both approaches provided the same optimal solution, with a 31% larger profit than the fixed input model.

The production of renewable fuels and chemicals is a critical component of global strategies to reduce greenhouse gas emissions. In this regard, pyrolysis oil obtained from biomass comprises hundreds of chemical compounds, thus rendering it a good precursor for manufacturing a variety of fuel products of commercial interest. Despite the large number of contributions describing the products' extraction, upgrading, and potential refining schemes, no bio-oil refinery is currently in operation. The main challenge in building a bio-oil refinery lies in the lack of an economically viable process configuration. Systematic studies comparing alternative refinery concepts, or configurations, are needed to identify the most promising configuration. To the best of our knowledge, this study is the first to use process graph (P-graph) methodology for the synthesis of pyrolysis oil refineries. In particular, this work shows the effectiveness of P-graph methodology in simultaneously calculating the profitability of various biorefinery designs by using data reported in the literature and providing information on how the introduction of new technologies to the database will impact the formation of profitable biorefinery concepts. Our work demonstrates a methodology for the addition of new unit operations to the database generated from the literature. The addition of a centrifuge for water extraction and a wet oxidation system for acetic acid production resulted in the generation of 330 biorefinery configurations, seven of which have a profitability ranging from $1,650 to $23,666/h (USD) with acetic acid and levoglucosan as the main products, respectively. This demonstrates that P-graph methodology is useful for discovering optimum techno-economic scenarios that may otherwise be overlooked.