384303 Global Optimization for Sustainable Design and Synthesis of Algae Processing Network for CO2 Mitigation and Biofuel Production Using Life Cycle Optimization

Monday, November 17, 2014: 12:47 PM
M303 (Marriott Marquis Atlanta)
Jian Gong and Fengqi You, Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL

Renewable biofuel is considered as a promising fossil fuel substitute due to its potential for large-scale production and carbon mitigation. Among various biomass feedstocks with increasing attention in the recent decades, algal biomass offers significant advantages over conventional energy crops [1]. Algae have high area productivity and accumulate lipid materials up to 80% of its dry cell weight. They are able to utilize non-arable land and waste water for cultivation. Additionally, the hydrocarbon algal biofuels converted via hydroprocessing accommodate the specifications of drop-in fuels, which can be directly incorporated into the existing infrastructure [2]. In order to reduce environmental impacts and improve the overall economic performance, systematic modeling and optimization frameworks can be utilized to identify and assess the sustainable design and synthesis of a processing network [3-5]. Therefore, applying such a framework to the algae processing network provides valuable insights into the technology pathways in the context of sustainability. The goal of this research is to identify and assess the optimal sustainable design and synthesis of an algae processing network with both economic and environmental concerns.

In this work, we propose a superstructure of an algae processing network including 7,800 processing routes and 11 processing sections, namely cultivation, harvesting, primary dewatering, secondary dewatering, storage, cell disruption & hydrothermal liquefaction, lipid extraction, upgrading, remnant treatment, electricity generation, and steam generation. Feed gas, off-gas, makeup nutrients, and recycled materials are fed into the cultivation system and mature algae product from cultivation is concentrated by several technologies. First, 10% of the dilute slurry is harvested by auto flocculation and dissolved air flotation.  The water content is further reduced by primary and secondary dewatering. Most of the reduced materials are recycled to the cultivation facility. In order to keep downstream facilities operating continuously, we employ a storage tank to temporarily store part of the enriched algae biomass for the production at night [6]. Next, the enriched microalgae paste undergoes cell disruption and lipid extraction or HTL so that most of the lipid materials are separated from algal cells. The lipid materials are then upgraded to biodiesel or renewable diesel depending on the specific conversion technologies. The rest of the algal body or remnant is fed to an anaerobic digester and decomposed into biogas, aqueous nutrients, and unreacted solids. Additionally, we couple electricity and steam generation into the network to satisfy the onsite energy consumption and reuse the resulting off-gas.

Based on this superstructure, a multi-objective mixed-integer nonlinear programming (MINLP) model for the sustainable design and synthesis is developed following the life cycle optimization frame work that integrates the multi-objective superstructure optimization scheme with life cycle assessment and techno-economic analysis [3, 4, 7-10]. The model consists of two objective functions and simultaneously minimize the unit annualized cost and the unit Global Warming Potential (GWP). As the final biofuel product can be biodiesel or renewable diesel depending on the specific upgrading technology, the unit objective functions are associated with the unit gasoline gallon equivalent (GGE) of the specific biofuel produced. To efficiently solve the problem, we propose a global optimization strategy that integrates a branch-and-refine algorithm based on successive piecewise linear approximations and an exact parametric algorithm based on Newton’s method [11-15]. The multi-objective optimization problem results in two Pareto-optimal curves for biofuel production and biological carbon sequestration, respectively. Three optimal processing routes are identified. From the biofuel production perspective, the most environmentally sustainable processing route achieved a unit GWP of 16.520 kg CO2-eq/GGE and a unit annualized cost of $9.712/GGE, while the most economical processing route resulted in a unit annualized cost of $7.017/GGE and a unit GWP of 26.791 kg CO2-eq/GGE. If the algae processing network is utilized for biological carbon sequestration, the unit carbon sequestration and utilization cost ranges from $10.43 /t of CO2 to $1.64 /t of CO2, corresponding to unit GWP’s from 352.41 kg CO2-eq/t of CO2 to 412.90 kg CO2-eq/t of CO2, respectively.


[1]        J. Ferrell and V. Sarisky-Reed, "National algal biofuels technology roadmap," US Department of Energy College Park, MD2010.

[2]        D. Yue, F. You, and S. W. Snyder, "Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges," Computers & Chemical Engineering, 2013.

[3]        D. J. Yue, M. A. Kim, and F. Q. You, "Design of Sustainable Product Systems and Supply Chains with Life Cycle Optimization Based on Functional Unit: General Modeling Framework, Mixed-Integer Nonlinear Programming Algorithms and Case Study on Hydrocarbon Biofuels," Acs Sustainable Chemistry & Engineering, vol. 1, pp. 1003-1014, Aug 2013.

[4]        B. Wang, B. H. Gebreslassie, and F. Q. You, "Sustainable design and synthesis of hydrocarbon biorefinery via gasification pathway: Integrated life cycle assessment and technoeconomic analysis with multiobjective superstructure optimization," Computers & Chemical Engineering, vol. 52, pp. 55-76, May 10 2013.

[5]        B. H. Gebreslassie, R. Waymire, and F. You, "Sustainable design and synthesis of algae-based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration," AIChE Journal, vol. 59, pp. 1599-1621, 2013.

[6]        J. Gong and F. You, "Optimal Design and Synthesis of Algal Biorefinery Processes for Biological Carbon Sequestration and Utilization with Zero Direct Greenhouse Gas Emissions: MINLP Model and Global Optimization Algorithm," Industrial & Engineering Chemistry Research, vol. 53, pp. 1563-1579, 2014/01/29 2014.

[7]        F. Q. You, L. Tao, D. J. Graziano, and S. W. Snyder, "Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input-output analysis," Aiche Journal, vol. 58, pp. 1157-1180, Apr 2012.

[8]        F. Q. You and B. Wang, "Life Cycle Optimization of Biomass-to-Liquid Supply Chains with Distributed-Centralized Processing Networks," Industrial & Engineering Chemistry Research, vol. 50, pp. 10102-10127, Sep 7 2011.

[9]        B. H. Gebreslassie, M. Slivinsky, B. L. Wang, and F. Q. You, "Life cycle optimization for sustainable design and operations of hydrocarbon biorefinery via fast pyrolysis, hydrotreating and hydrocracking," Computers & Chemical Engineering, vol. 50, pp. 71-91, Mar 5 2013.

[10]      Q. Zhang, J. Gong, M. Skwarczek, D. Yue, and F. You, "Sustainable process design and synthesis of hydrocarbon biorefinery through fast pyrolysis and hydroprocessing," AIChE Journal, vol. 60, pp. 980-994, 2014.

[11]      F. Q. You and I. E. Grossmann, "Stochastic Inventory Management for Tactical Process Planning Under Uncertainties: MINLP Models and Algorithms," Aiche Journal, vol. 57, pp. 1250-1277, May 2011.

[12]      Z. X. Zhong and F. Q. You, "Globally convergent exact and inexact parametric algorithms for solving large-scale mixed-integer fractional programs and applications in process systems engineering," Computers & Chemical Engineering, vol. 61, pp. 90-101, Feb 11 2014.

[13]      F. Q. You, J. M. Pinto, I. E. Grossmann, and L. Megan, "Optimal Distribution-Inventory Planning of Industrial Gases. II. MINLP Models and Algorithms for Stochastic Cases," Industrial & Engineering Chemistry Research, vol. 50, pp. 2928-2945, Mar 2 2011.

[14]      D. J. Yue and F. Q. You, "Planning and Scheduling of Flexible Process Networks Under Uncertainty with Stochastic Inventory: MINLP Models and Algorithm," Aiche Journal, vol. 59, pp. 1511-1532, May 2013.

[15]      F. Q. You, P. M. Castro, and I. E. Grossmann, "Dinkelbach's algorithm as an efficient method to solve a class of MINLP models for large-scale cyclic scheduling problems," Computers & Chemical Engineering, vol. 33, pp. 1879-1889, Nov 12 2009.

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See more of this Session: Advances in Life Cycle Optimization for Process Development
See more of this Group/Topical: Environmental Division