CHEMICAL ENGINEERING TRANSACTIONS VOL. 45, 2015 A publication of The Italian Association of Chemical Engineering www.aidic.it/cet Guest Editors: Petar Sabev Varbanov, Jiří Jaromír Klemeš, Sharifah Rafidah Wan Alwi, Jun Yow Yong, Xia Liu Copyright © 2015, AIDIC Servizi S.r.l., ISBN 978-88-95608-36-5; ISSN 2283-9216 DOI:10.3303/CET1545272 Please cite this article as: Holaysan S.A.K., Razon L.F., Tan R.R., 2015, Development of a modified luus-jaakola adaptive random search algorithm for design of integrated algal bioenergy system, Chemical Engineering Transactions, 45, 1627- 1632 DOI:10.3303/CET1545272 1627 Development of a Modified Luus-Jaakola Adaptive Random Search Algorithm for Design of Integrated Algal Bioenergy System Sed A. K. Holaysan, Luis F. Razon, Raymond R. Tan* Chemical Engineering Department, De La Salle University, 2401 Taft Avenue, Manila Philippines raymond.tan@dlsu.edu.ph Process systems engineering (PSE) approaches are useful for facilitating the optimal design and operation of industrial plants. This study develops a modified Luus-Jaakola adaptive random search (LJ-ARS) procedure by incorporating some features from the line-up competition algorithm (LCA). The search procedure is conducted using multiple points, and cooperation is exhibited as each point moves toward the next-best point to improve its position. The search space of each point is influenced by its rank, but a lower limit for the space reduction factor is specified to prevent premature convergence. A probabilistic rounding- off procedure is used for integer variables, while the penalty function approach is used for constraint resolution. This modified algorithm is encoded in Microsoft Excel and Visual Basic for Applications and is used to optimize a mixed-integer nonlinear programming model of an integrated algal bioenergy system, while the original LJ-ARS is unable to locate a feasible solution. The model considers six processes: cultivation of the microalgae Chlorella vulgaris, dewatering, cell disruption, pretreatment, oil extraction, and transesterification. The optimal solution, which has been verified using LINGO 14.0, involves microfiltration (for dewatering) and oven drying, but does not utilize any cell disruption process due to high capital cost and energy requirement. This implies that if residual biomass can be sold, it may be more economical to cultivate more algae than to increase the oil yield by means of cell disruption. Furthermore, it is essential to utilize the residual biomass to ensure that the system produces more energy than it consumes. Finally, it is more economical to use residual biomass to supply energy rather than to sell the residual biomass while purchasing electricity. 1. Introduction Biomass is known as a carbon neutral fuel, because the CO2 it emits upon combustion is originally fixed from the atmosphere during growth. The technology required for production of microalgal biomass is already sufficient (Chisti, 2007), although exclusive production of biodiesel may be economically infeasible (Pinzon et al., 2014). Razon and Tan (2011) showed that the net energy ratio (energy output divided by energy input) for production of microalgal biofuel could be less than 1, implying that certain processing pathways consume more energy than they produce. Process systems engineering (PSE) techniques are used to model and optimize bioenergy systems such as biomass gasification (Sun et al., 2014). If a process system is represented as a linear model, location of the optimal solution can be guaranteed using standard methods such as the simplex algorithm. However, significant computational difficulties may be encountered in the optimization of nonlinear models (Martelli and Amaldi, 2013). According to Jenkins (1997), the assumption of constant cost scaling for bioenergy systems cannot be made without exact data. An MINLP model of a biorefinery by Zondervan et al. (2011) considered 72 process options for various stages such as pre-treatment, fermentation processes, separation processes, and fuel blending. Various algorithms for optimization of NLP/MINLPs are currently in use, and metaheuristic or stochastic techniques are one such class of algorithms. One of these techniques is direct search or adaptive random search by Luus and Jaakola (1973). Variations of the original Luus-Jaakola method have been proposed through addition of region collapse and tolerance value parameters (Luus