Conference Agenda

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Session Overview
ET-2: Tools for assessing energy systems
Wednesday, 26/Jun/2019:
2:00pm - 3:30pm

Session Chair: Stefano Cucurachi
Location: Weidler/Halsey

2:00pm - 2:20pm

Techno-economic Analysis and Life Cycle Assessment for Biosorption of Rare Earth Elements from Coal by-products

Majid Alipanah Doolabi1, Hongyue Jin1, Dan M. Park2, Aaron W. Brewer2, Yongqin Jiao2

1University of Arizona, United States of America; 2Lawrence Livermore National Laboratory, United States of America

Rare-earth elements (REEs) play a critical role in today’s technologies such as modern communications, advanced transportation systems, and renewable energy production. China is responsible for producing more than 85% of the global REEs, and the U.S. is 100% dependent on import. Due to the risk associated with Chinese near-monopolistic supply, the U.S. is looking for securing domestic REE supply by exploring alternative REE sources and new extraction methods with high efficiency. Herein, a novel biosorption technology being developed by our team is examined for REE recovery from coal by-products, an abundance feedstock with a high REE content, ranging between 340-2800 ppm. First, coal byproducts undergo physical upgrading and acid leaching, while bioengineered microbes are cultured and prepared for REE adsorption. A continuous-flow bioreactor is designed to allow the immobilized microbes to selectively adsorb REEs from the leachate. Upon biomass harvest, sodium citrate is applied to desorb REEs, which are further purified through oxalic precipitation and calcination to obtain 95+% pure total rare earth oxides (TREOs). For microbe immobilization, we considered two alternative microbe carriers: biofilm and microbe bead system.

Techno-economic analysis (TEA) was performed to assess the economic performance of the biosorption technology, projecting industrial scale operation from lab-scale data. Both carrier systems were analyzed and compared with other competing technologies with low- and high-grade coal by-products. TEA results of biofilm carrier were estimated to be profitable, while leaching was the most costly process. Furthermore, TEA results for the microbe bead system showed that microbe bead adsorption capacity and reuse time are the two main factors which constitute more than 60 percent of the entire process costs. Sensitivity analysis revealed break-even conditions and future improvement opportunities. For example, if we could achieve REE adsorption capacity of 5 mg per gram of dry beads and reuse the microbe beads for more than 12 times, the process could be profitable. Experimental evidence suggested high likelihood to meet these thresholds.

Life cycle analysis (LCA) was performed to assess the environmental performance and assist with sustainable development of the technology. Overall, REE biosorption was found to offer environmental benefits compared to the alternative technologies. The environmental hotspots were identified to be sulfuric acid and limestone, which were consumed for leaching and pH adjustment, respectively. Alternative approaches are tested to further reduce the environmental impacts by recycling and reuse of the acid, for example.

The project aimed to study the economic and environmental feasibility of integrating biosorption for REE recovery from coal byproducts. Preliminary TEA and LCA results demonstrated that the project would be profitable and environmentally friendly, which warrants further development of the biosorption technology.

2:20pm - 2:40pm

Quantifying Energy Demand and GHG Emissions of Activated Carbon Production from Diverse Woody Biomass: An Predictive Modeling Framework of Artificial Neural Network and Kinetic Based Simulation

Mochen Liao, Stephen Kelley, Yuan Yao

North Carolina State University, United States of America

The utilization of biomass to replace coal as the feedstock for activated carbon has become attractive in recent years due to its potential to reduce process energy demands and greenhouse gas (GHG) emissions.1 A few studies have evaluated the energy demand and GHG emissions of steam AC production through life cycle assessment (LCA).1–3 However, these studies have been limited to a few specific feedstocks (e.g. coconut shell, wood waste) and operational conditions, which may not be applied to a wide range of biomass feedstock. A predictive model that can quantify the energy demand and GHG emissions of AC made from diverse biomass will be helpful for decision-makers to screen different feedstock.

In this work, a predictive model for energy demand and GHG emissions associated with AC production is developed by integrating process-based simulation, Artificial Neural Network (ANN) and pyrolysis kinetic model. A large dataset of biomass characterization, operational conditions of pyrolysis and steam activation, and AC yields, were collected from literature and used for ANN training and validation. Given the heterogeneity of biomass characterization data available for different types of biomass, this initial work focuses on woody biomass. The trained ANN is able to predict key process parameters such as yields based on the input data of biomass characterization and major operational conditions.4 Aspen Plus simulation was developed based on predicted yield and the kinetic model5 to generate life cycle inventory data such as energy and mass balance, and GHG emissions.

The integrated modeling framework is able to estimate the energy demand and GHG emissions of AC produced from woody biomass. The results can be used to screen woody feedstocks potentially useful for production of AC and optimize the process conditions. The impact of feedstock properties on the process energy demand and GHG emissions will be discussed in the presentation, which will provide useful information for biomass selection in various industrial application. For example, we found that as the hydrogen content of the woody biomass increased the energy demand increase due to a decrease on the mass yield. In addition, the integrated methods, e.g., kinetic modeling, ASPEN modeling, and ANN, developed in this work have the potential to be applied to other manufacturing processes, especially emerging technologies that lack LCI data.


1. Arena N, Lee J, Clift R. Life Cycle Assessment of activated carbon production from coconut shells. J Clean Prod. 2016;125:68–77.

2. Gu H, Bergman R, Anderson N, Alanya-Rosenbaum S. Life Cycle Assessment of Activated Carbon From Woody Biomass. Wood Fiber Sci. 2018;50(3):1–15.

3. Kim MH, Jeong IT, Park SB, Kim JW. Analysis of environmental impact of activated carbon production from wood waste. Environ Eng Res. 2018;24(1):117–26.

4. Liao M, Kelley SS, Yao Y. Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass. Biofuels, Bioprod Biorefining. 2019;(Accepted).

5. Anca-Couce A, Sommersacher P, Scharler R. Online experiments and modelling with a detailed reaction scheme of single particle biomass pyrolysis. J Anal Appl Pyrolysis. 2017;127(July):411–25.

2:40pm - 3:00pm

Transportation Network Companies Have Improved Personal Transportation Energy Intensity Across and Within U.S. Metropolitan Areas: A Combined Clustering and Regression Analysis

Jacob Ward, Jeremy Michalek, Inês Azevedo, Costa Samaras, Pedro Ferreira

Carnegie Mellon University, United States of America

Transportation network companies (TNCs), such as Uber and Lyft, that provide on-demand mobility services, have already changed how many urban travelers move. Despite rapid TNC growth in recent years, there is limited knowledge to-date on how they influence energy consumption and how that effect may vary as a function of underlying urban forms. Depending on the urban context in which these services operate, TNCs may either increase or decrease the energy intensity of personal transportation by shifting travel into TNC vehicles with systematically different efficiencies and/or alternative powertrains, such as electric vehicles (EVs). TNCs may also affect aggregate transportation energy consumption by either increasing or decreasing the number of vehicles on the road. To address these knowledge gaps, we aim to systematically categorize U.S. metropolitan areas and quantitatively estimate the net causal effects of recent TNC entry on vehicle adoption and energy consumption in different urban typologies.

We combine machine learning and statistical classification methods with a difference-in-difference propensity score-weighted regression model to estimate both the aggregate effect of TNCs on energy-relevant vehicle characteristics, like fuel economy and electric vehicle prevalence, in different clusters of U.S. metropolitan areas, as well as the intra-urban effects on these same outcomes across different ZIP codes types. Using annual data from 2010 to 2017 on the over 240 million personal vehicles registered in the U.S., we find an 1.8% increase in the number of vehicles registered in the average metropolitan area. Furthermore, while we find no significant change in EV registrations at the metropolitan level, we do find significant increases in EV registration percentages, ranging from a fraction of a percentage point to as high as 50 percentage points (and no significant declines in EV registrations are found). While fuel economy analysis is ongoing, we can already estimate these changes to the number, efficiency, and fuel source(s) of personal vehicles on U.S. roads offer energy security and net externality benefits to the U.S. economy in the range of $20 million to $800 million.

3:00pm - 3:20pm

How Technology Assessment Tools Can Inform Life Cycle Assessments of Emerging Technologies

Sylvia Sleep, Alireza Aslani, Joule Bergerson

Department of Chemical and Petroleum Engineering, University of Calgary

There is increasing attention being drawn by life cycle assessment (LCA) practitioners to challenges associated with assessing the impacts of emerging technologies [1]. Significant progress has been made to addressing uncertainties in LCAs of commercial-scale technologies, however, those conducting LCAs of emerging technologies face additional challenges including data availability, scale-up, comparability (i.e., what market the technology will be deployed in, which incumbent the emerging technology should be compared to), and uncertainty [1]. While recent literature has drawn attention to these challenges, little work has been done to develop methods that can applied in these scenarios to improve the robustness of LCA results. Improving upon these methods can increase the accessibility and utility of the insights generated by an LCA, at a stage in technology development where there are many opportunities to affect future performance of the technology.

On the technology management side, a broad range of technology assessment tools (TATs) are being applied for diffusion, launching, adaptation, and financing of emerging technologies to inform technology developers and policymakers about the tradeoffs associated with emerging technologies at various stages of commercialization [2]. TATs can help by: forecasting technological developments, impacts, and consequences (awareness TATs), supporting actors or groups in formulating policies or strategies (strategic TATs), broadening decision-making about technological development (constructive TATs), and backcasting to develop scenarios of desirable futures that can be used as a starting point in the innovation process [3]. In this work, we present an overview of TATs and discuss opportunities for combining these tools with LCAs or applying these tools to help inform LCA’s goal and scope definition stages. We provide recommendations for specific tools that can be applied or combined with LCA of technologies at various technology readiness levels.

Our review shows that TATs are diverse and range from traditional analytic method to more descriptive and qualitative methods [2,3]. The specific TATs to apply depends on the level and nature of uncertainties in the LCA of an emerging technology. For example, descriptive TATs such as Technology Balance Sheet and Technology Trees can improve an LCA’s goal and scope definition, help to define reasonable scenarios, and set appropriate assumptions for the analysis. Techniques related to system optimization and decision making can be used for inventory analysis, data validation, and aggregation. Backcasting techniques such as Roadmapping [4] can be combined with the interpretation phase of LCA to inform decision-making by technology developers. The general discussion in LCA of emerging technologies has focused on the goal and scope definition and inventory analysis. Future work could explore opportunities to combine TATs such as System Dynamics Modelling or Externalities Analysis in the impact assessment stages [5].

[1] Hetherington AC, Borrion AL, Griffiths OG, McManus MC. Use of LCA as a development tool within early research: Challenges and issues across different sectors. doi:10.1007/s11367-013-0627-8.

[2] Tran TA, Daim T. A taxonomic review of methods and tools applied in technology assessment. doi:10.1016/j.techfore.2008.04.004.

[3] Van Den Ende J, Mulder K, Knot M, Moors E, Vergragt P. Traditional and Modern Technology Assessment: Toward a Toolkit. doi:10.1016/S0040-1625(97)00052-8.

[4] Hussain M, Tapinos E, Knight L. Scenario-driven roadmapping for technology foresight. doi:10.1016/j.techfore.2017.05.005.

[5] Bichraoui-Draper N, Xu M, Miller Sh, Guillaume B. Agent-based life cycle assessment for switchgrass-based bioenergy systems. doi:10.1016/j.resconrec.2015.08.003