A P-graph Approach for Planning Sustainable Rice Straw Management Networks
Maria Victoria Migo-Sumagang and Michael Angelo B. Promentilla
Received: April 18, 2022/ Revised: April 4, 2024/ Accepted: June 30, 2024
The Philippines produces up to 15.2 Mt of rice straw waste annually. Unmanaged rice straw waste disposal can lead to pollution from open field burning. On the other hand, rice straw agricultural waste can be used sustainably to produce valuable products such as mushrooms, fodder, pellets, or bioenergy, in a rice straw management system. Such systems can be optimized using Process Integration tools so that the raw materials are used efficiently at the maximum profit and with a minimal carbon footprint. The P-graph framework is an efficient Process Integration tool that solves Process Network Synthesis problems. A P-graph finds the optimal and sub-optimal solutions for further analysis, which is useful in decision-making. This work developed a P-graph model for a rice straw management network considering the straw collection and storage steps and the production of both bioenergy and non-bioenergy products. The model can generate optimal and sub-optimal solutions (based on profit) and can simulate raw material disruption scenarios. The model is demonstrated through a case study on three rice straw fields with a maximum total rice straw yield of 96.84 t/yr. The case study considered the operating and raw material costs but did not consider the fixed and investment costs in the calculation of the profit. The results show that mushroom production using rice straw as substrate is the optimal solution with a potential profit of US$ 14 659.60/yr, followed by pellet production with a potential profit of US$ 12 627.90/yr. Disruption scenarios at reduced diesel, manual labor, and rice straw show that mushroom production is still the optimum solution, showing the robustness of the solution. This basic model shows that P-graphs can be applied to rice straw management networks to aid with decision-making for sustainability. Caution must be exercised as the results are context- and location-specific.