@article{sardarmehni_anchieta_levis_2022, title={Solid waste optimization life-cycle framework in Python (SwolfPy)}, volume={1}, ISSN={["1530-9290"]}, url={https://doi.org/10.1111/jiec.13236}, DOI={10.1111/jiec.13236}, abstractNote={This paper describes a novel open‐source life‐cycle optimization framework for solid waste and sustainable materials management applications named solid waste optimization life‐cycle framework in Python (SwolfPy). The current version includes life‐cycle models for landfills, mass burn waste‐to‐energy, gasification, centralized composting, home composting, anaerobic digestion, material recovery facilities, refuse‐derived fuel facilities, material recycling, transfer stations, and single‐family collection. Compared to existing frameworks, SwolfPy streamlines data input/output processes, improves model integration and modularity, provides a wide variety of data visualization and customization, speeds up uncertainty analysis and optimization, and has a user‐friendly graphical user interface (GUI). SwolfPy's GUI allows users to define solid waste management networks and scenarios as well as perform comparative life cycle assessments (LCAs), contribution analyses, uncertainty analyses, and optimization. SwolfPy is implemented in Python using Pandas, NumPy, and SciPy for computational tasks, PySide2 for creating the GUI, and Brightway2 for storing life‐cycle inventory data and performing the LCA calculations. SwolfPy is modular and flexible, which enables it to be easily coupled with other packages and to facilitate the addition of new processes, materials, environmental flows and impacts, and methodologies. SwolfPy uses sequential least‐squares programming for constrained nonlinear optimization to find systems and strategies that minimize cost or environmental emissions and impacts while meeting user‐defined constraints. An illustrative case study with 44 materials, 4 collection processes, and 6 treatment processes is presented, and SwolfPy performs 10,000 Monte Carlo iterations in 16 min and finds optimal solutions in 10–25 min on a Windows 10 machine with a CPU speed of 3.60 GHz and 8 logical processors. This article met the requirements for a Gold‐Gold Badge. JIE data openness badge described at http://jie.click/badges.}, journal={JOURNAL OF INDUSTRIAL ECOLOGY}, publisher={Wiley}, author={Sardarmehni, Mojtaba and Anchieta, Pedro H. Chagas and Levis, James W.}, year={2022}, month={Jan} }
 @article{sardarmehni_levis_2021, title={Life-cycle modeling of nutrient and energy recovery through mixed waste processing systems}, volume={169}, ISSN={["1879-0658"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85102536147&partnerID=MN8TOARS}, DOI={10.1016/j.resconrec.2021.105503}, abstractNote={There is increasing interest in recovering nutrients and energy from the organic fraction of municipal solid waste (OFMSW). Given the costs associated with separate collection of OFMSW, and the potential difficulty in finding clean feedstocks, there are potential benefits in beneficial recovery of OFMSW as part of residual MSW. Therefore, this study compared the life-cycle impacts associated with management alternatives for recovering energy and/or nutrients from the OFMSW through mixed waste processing systems. The considered treatment alternatives include landfilling, mass burn waste-to-energy, gasification and syngas combustion (GC) for electricity production, gasification Fischer–Tropsch (GFT) for transportation fuel production, aerobic composting (AC), and anaerobic digestion (AD). Seven environmental impacts include global warming potential (GWP), cumulative energy demand, acidification, eutrophication, photochemical oxidation, ecotoxicity, and human toxicity were assessed for five sets of state and one U.S. national waste compositions. The mass burn waste-to-energy and GC scenarios generally have the lowest environmental impacts, while landfilling and GFT have the greatest impacts. Separating out organics for AC increased environmental impacts compared to sending them to GC, while sending them to AD decreased GWP and increased the other impacts. Sensitivity analyses suggest that these conclusions are generally robust to uncertainty in input values.}, journal={RESOURCES CONSERVATION AND RECYCLING}, author={Sardarmehni, Mojtaba and Levis, James W.}, year={2021}, month={Jun} }
 @article{sardarmehni_levis_barlaz_2021, title={What Is the Best End Use for Compost Derived from the Organic Fraction of Municipal Solid Waste?}, volume={55}, ISBN={1520-5851}, url={https://doi.org/10.1021/acs.est.0c04997}, DOI={10.1021/acs.est.0c04997}, abstractNote={There is increasing interest in diverting the organic fraction of municipal solid waste from landfills to biological treatment processes that result in compost. Due to variations in compost quality and available markets, it is not always possible for compost to be beneficially used on soil. In such cases, compost may be used as alternative daily cover (ADC) in landfills. The objective of this study is to compare the environmental impacts of using compost as a soil amendment, accounting for its beneficial substitutions for fertilizer and peat, to its use as ADC. Monte Carlo simulation and parametric sensitivity analyses were performed to evaluate the effects of uncertainty in input values on the environmental performance. The ADC scenario outperforms the soil amendment scenario in terms of global warming potential, acidification, and eutrophication in ∼63, ∼77, and ∼100% of simulations, respectively, while the soil amendment scenario is better in terms of cumulative energy demand and abiotic resource depletion potential ∼94 and ∼96% of the time, respectively. Therefore, we recommend that using compost as ADC be considered, especially when site-specific factors such as feedstock contamination or a lack of markets make it difficult to find appropriate applications for compost as a soil amendment.}, number={1}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={American Chemical Society (ACS)}, author={Sardarmehni, Mojtaba and Levis, James W. and Barlaz, Morton A.}, year={2021}, pages={73–81} }
 @article{sardarmehni_tahouni_panjeshahi_2017, title={Benchmarking of energy saving potential and CO<inf>2</inf> reduction in iranian compressor stations}, volume={61}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85030776137&partnerID=MN8TOARS}, DOI={10.3303/CET1761229}, journal={Chemical Engineering Transactions}, author={Sardarmehni, M. and Tahouni, N. and Panjeshahi, M.H.}, year={2017}, pages={1387–1392} }
 @article{sardarmehni_tahouni_panjeshahi_2017, title={Benchmarking of olefin plant cold-end for shaft work consumption, using process integration concepts}, volume={127}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85017099205&partnerID=MN8TOARS}, DOI={10.1016/j.energy.2017.03.066}, abstractNote={The olefin plant is all about separating cracked gas into ethylene, propylene and other heavier by-products using low-temperature gas separation processes. This process is energy-intensive and hence retrofitting for energy saving would be desirable. However, a full retrofit study requires a lot of time and costly engineering work. So, a novel method is introduced in this paper for benchmarking of shaft work consumption in olefin cold-end, which is based on process integration concepts. In developing this method, the amount of shaft work required in refrigeration cycles was first targeted via application of Pinch Analysis to six different olefin plants followed by the calculation of feasible and achievable energy saving potential. When doing so, the effect of predominant factors such as plant capacity, feedstock (naphtha or natural gas), products specification and type of technology being used was investigated as well. Finally, a mathematical model was developed for rapid estimation of energy saving potential using the above key factors. This model was verified through case studies and was proved to be accurate enough for shortcut calculations.}, journal={Energy}, author={Sardarmehni, M. and Tahouni, N. and Panjeshahi, M.H.}, year={2017}, pages={623–633} }