Fengqi You

Assumptions and the levelized cost of energy for photovoltaics

Photovoltaic electricity is a rapidly growing renewable energy source and will ultimately assume a major role in global energy production. The cost of solar-generated electricity is typically compared to electricity produced by traditional sources with a levelized cost of energy (LCOE) calculation. Generally, LCOE is treated as a definite number and the assumptions lying beneath that result are rarely reported or even understood. Here we shed light on some of the key assumptions and offer a new approach to calculating LCOE for photovoltaics based on input parameter distributions feeding a Monte Carlo simulation. In this framework, the influence of assumptions and confidence intervals becomes clear.

Sustainable scheduling of batch processes under economic and environmental criteria with MINLP models and algorithms

Abstract We address the bi-criterion optimization of batch scheduling problems with economic and environmental concerns. The economic objective is expressed in terms of productivity, which is the profit rate with respect to the makespan. The environmental objective is evaluated by means of environmental impact per functional unit based on the life cycle assessment methodology. The bi-criterion optimization model is solved with the e -constraint method. Each instance is formulated as a mixed-integer linear fractional program (MILFP), which is a special class of non-convex mixed-integer nonlinear programs. In order to globally optimize the resulting MILFPs effectively, we employ the tailored reformulation-linearization method and Dinkelbachs algorithm. The optimal solutions lead to a Pareto frontier that reveals the tradeoff between productivity and environmental impact per functional unit. To illustrate the application, we present two case studies on the short-term scheduling of multiproduct and multipurpose batch plants.

Integrating Hybrid Life Cycle Assessment with Multiobjective Optimization: A Modeling Framework

By combining life cycle assessment (LCA) with multiobjective optimization (MOO), the life cycle optimization (LCO) framework holds the promise not only to evaluate the environmental impacts for a given product but also to compare different alternatives and identify both ecologically and economically better decisions. Despite the recent methodological developments in LCA, most LCO applications are developed upon process-based LCA, which results in system boundary truncation and underestimation of the true impact. In this study, we propose a comprehensive LCO framework that seamlessly integrates MOO with integrated hybrid LCA. It quantifies both direct and indirect environmental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria. The proposed LCO framework is demonstrated through an application on sustainable design of a potential bioethanol supply chain in the UK. Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO. Among the biomass feedstock options considered, using woody biomass for bioethanol production would be the most preferable choice from a climate perspective, while the mixed use of wheat and wheat straw as feedstocks would be the most cost-effective one.

Structures and adsorption of binary hard-core Yukawa mixtures in a slitlike pore: Grand canonical Monte Carlo simulation and density-functional study

The grand canonical ensemble Monte Carlo simulation and density-functional theory are applied to calculate the structures, local mole fractions, and adsorption isotherms of binary hard-core Yukawa mixtures in a slitlike pore as well as the radial distribution functions of bulk mixtures. The excess Helmholtz energy functional is a combination of the modified fundamental measure theory of Yu and Wu [J. Chem. Phys. 117, 10156 (2002)] for the hard-core contribution and a corrected mean-field theory for the attractive contribution. A comparison of the theoretical results with the results from the Monte Carlo simulations shows that the corrected theory improves the density profiles of binary hard-core Yukawa mixtures in the vicinity of contact over the original mean-field theory. Both the present corrected theory and the simulations suggest that depletion and desorption occur at low temperature, and the local segregation can be observed in most cases. For binary mixtures in the hard slitlike pore, the present corrected theory predicts more accurate surface excesses than the original one does, while in the case of the attractive pore, no improvement is found in the prediction of a surface excess of the smaller molecule.

Deciphering the true life cycle environmental impacts and costs of the mega-scale shale gas-to-olefins projects in the United States

This paper addresses the techno-economic-environmental analysis of large-scale olefin production from shale gas in the major shale regions of the U.S. (including Appalachian, Gulf Coast, Mid-Continent, and Rocky Mountain regions) and investigates its environmental footprints. To decipher the true production costs and environmental impacts, we first develop shale gas supply and olefin production network models to estimate pipeline distances, numbers of wells, well-sites, and gathering systems needed in the near- and mid-term. Next, detailed process design, modeling, and integration methods for alternative technologies are developed. We conduct life cycle analysis (LCA) to systematically evaluate the energy–water–carbon nexus. Based on the economic and LCA results, we compare the influences of gas composition, project operating time, well lifetime, and the allocation method. The results indicate that the four shale regions considered would in total supply feedstocks for U.S. ethylene production for at least 130 years. However, only olefins produced from Gulf Coast and Mid-Continent regions demonstrate economic advantage (

Design and optimization of shale gas energy systems: Overview, research challenges, and future directions

668 per t and

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

255 per t) over ethylene in the current market. Based on the mass-based allocation approach, for the four shale regions evaluated, the energy consumption is 13.8–17.2, 14.3–16.7, 13.3–16.7, and 12.2–14.5 GJ per t olefins, and the freshwater footprint is 3.31–4.28, 5.34–5.65, 3.05–3.56, and 4.68–5.03 kg kg−1 olefins, respectively. In addition, normalized GHG emissions indicate that shale gas can be categorized as a low-carbon feedstock (0.75–1.05 kg CO2-eq per kg) based on a mass-based allocation approach, or a high-carbon feedstock (1.24–2.13 kg CO2-eq per kg) based on an economic value-based allocation approach.

Stackelberg-game-based modeling and optimization for supply chain design and operations: A mixed integer bilevel programming framework

Abstract The “shale revolution” has been a game changer in the global energy market and raised the importance of optimal design and operations of shale gas energy systems. This article highlights key challenges and identifies potential research opportunities in the corresponding area. A brief introduction of shale gas energy systems and their significant impacts are first presented, followed by a comprehensive overview and classification of relevant publications. Based on the literature review, we further investigate and discuss the research challenges of developing integrated, sustainable shale gas energy systems under the guidance of “triple bottom line” and integrated approaches of material flow analysis and life cycle optimization. Leveraging the large amount of data from shale gas industry, data-driven optimization methods could open up new research opportunities for hedging against uncertainty in design and operations of shale gas energy systems. Moreover, potential opportunities are explored by introducing game theory into modeling and optimization of decentralized shale gas systems with multiple stakeholders. The benefits of enabling emerging technologies/operations are discussed as well.

A systematic simulation-based process intensification method for shale gas processing and NGLs recovery process systems under uncertain feedstock compositions

A genetic algorithm was used to accelerate the computational discovery of new nanoporous materials for capturing CO2. Discovery of new adsorbent materials with a high CO2 working capacity could help reduce CO2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO2 capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO2 working capacity and a high CO2/H2 selectivity. One of the synthesized MOFs shows a higher CO2 working capacity than any MOF reported in the literature under the operating conditions investigated here.

Data-driven robust optimization based on kernel learning

Abstract While Stackelberg leader–follower games and bilevel programming have become increasingly prevalent in game-theoretic modeling and optimization of decentralized supply chains, existing models can only handle linear programming or quadratic programming followers’ problems. When discrete decisions are involved in the followers problem, the resulting lower-level mixed-integer program prohibits direct transformation of the bilevel program into a single-level mathematical program using the KKT conditions. To address this challenge, we propose a mixed-integer bilevel programming (MIBP) modeling framework and solution algorithm for optimal supply chain design and operations, where the follower is allowed to have discrete decisions, e.g., facility location, technology selection, and opening/shutting-down of production lines. A reformulation-and-decomposition algorithm is developed for global optimization of the MIBP problems. A case study on an integrated forestry and biofuel supply chain is presented to demonstrate the application, along with comparisons to conventional centralized modeling and optimization methods.