Intelligent resource management and decision support system for sustainable refuse-derived fuel production
Creators: Tahir, Junaid Oosman
Publisher: name: University of Alberta Library
Year: 2024

<p>Municipal Solid Waste (MSW) represents a diverse array of materials promising substantial potential for reuse and recycling. MSW originates from residential, commercial, and construction sources and is generally divided into recyclable and non-recyclable categories. Specifically, within the non-recyclable portion lies the organic component, which can be utilized to harness energy. This reclaimable combustible segment, extracted from MSW, is termed “Refuse Derived Fuel” (RDF). Processes like RDF incineration convert this material into a range of valuable resources. This transformative process yields heat, electricity, and a diverse array of biofuels through gasification and pyrolysis. Among these biofuels are biomethane, dimethyl ether, methanol, syngas, bio-oil, biochar, etc. However, lack of operating plant data, uncertainties in process inputs, operating parameters, capital investments, waste composition, and product costs are some of the critical parameters that influence the performance indicator for waste treatment systems. In particular, the waste treatment system transforming municipal solid waste into RDF faces limitations in maintaining consistent production and quality control standards of RDF. The leading cause is the unwary biomass fuel supply chain decision-making, implicating many decisions associated with discovering the best waste diversion options and measuring their impact on the waste processing plants. Hence, municipal solid waste management requires integrated decision-making for sustainable waste treatment systems. Rigorous assessment is crucial for improving operational planning and addressing uncertainties in waste-to-energy applications.<br>
The proposed study aims to tackle the challenges of assessing waste treatment system related to the RDF production at a material recovery facility (MRF) while integrating varied uncertainties. A decision support system is proposed for sustainable RDF production connecting four key components to form a comprehensive framework tailored for management and operational level hierarchies at any MRF. The framework includes, first an advanced computer vision system, developed to enhance the workflow of the waste characterization process at an MRF. This system enables precise waste detection and early mitigation planning for unsuitable compositions in RDF production. Secondly, the prior phase is integrated to a discrete simulation model, examining various production line configurations for high-quality RDF and consistent mass flow efficiency. These simulations yield optimal process plant configurations, ensuring alignment with specified RDF quality benchmarks. Third, knowing the calorific value aids in optimizing combustion for maximum energy extraction, assessing fuel quality for suitable applications, and estimating emissions for cleaner energy systems. Using chemical analysis, real-world experiments are executed to develop calorific value prediction models for processed RDF. These models aid operational decision-making and are cross- validated with existing ones for accuracy. Lastly, study integrates risk epidemiology into the Public-Private-Partnership (PPP) model to assess RDF plant economics and introduces a quantitative energy from waste (EfW) feasibility model, accounting for subjective biases in risk perceptions of groups involved in PPP. </p>

<p>The contributions of this study lay the foundation of efficient problem-solving and scientific solution methods for effective decision-making in waste management. This study&#39;s insights benefit various stakeholders profoundly. Managers at MRFs gain the ability to assess diverse scenarios, ensuring robust configurations amid input uncertainties. Operators aiming to elevate profits and RDF material quality find strategic guidance in these scenarios. Local and regional waste managers benefit from efficiency parameter modeling, enhancing waste stream redirection to facilities optimizing sorting. Policymakers, often facing knowledge gaps, find clarity in this study regarding material sorting intricacies and impacts of recycling policy. The proposed research can be extended to investigate the RDF production problems considering additional uncertain factors, such as fluctuations in waste composition and variability in market demand for RDF, as well as dynamic events like operational disruptions and policy changes in future work.</p>