Swarm-Assisted Investment Planning of a Bioethanol Plant
Grzegorz Redlarski1, Marek Krawczuk1, Adam Kupczyk2, Janusz Piechocki3, Dominik Ambroziak1, Aleksander Palkowski1
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1Department of Mechatronics and High-Voltage Engineering, Gdansk University of Technology,
G. Narutowicza 11/12, 80-233 Gdansk, Poland
2Department of Production Management and Engineering, Warsaw University of Life Sciences,
Nowoursynowska 164, 02-787 Warsaw, Poland
3Department of Electrical Engineering, Power Engineering, Electronics, and Control Engineering,
University of Warmia and Mazury, M. Oczapowskiego 11, 10-719 Olsztyn, Poland
Submission date: 2016-11-07
Final revision date: 2016-12-22
Acceptance date: 2016-12-29
Online publication date: 2017-05-15
Publication date: 2017-05-26
Pol. J. Environ. Stud. 2017;26(3):1203–1214
Bioethanol is a liquid fuel for which a significant increase in the share of energy sources has been observed in the economies of many countries. The most significant factor in popularizing bioethanol is the profitability of investments in construction of facilities producing this energy source, as well as the profitability of its supply chain. With the market filled with a large amount of equipment used in the bioethanol production process, it is often difficult to make an optimal decision regarding the investment. Another issue is the location of the plant itself. Economic benefits are strongly associated with costs of equipment and materials, the amount of revenue from sales, and transportation costs. This article presents an attempt to solve this problem by using several swarm algorithms – new and fast-growing optimisation techniques. By employing ant colony optimization, river formation dynamics, particle swarm optimization, and cuckoo search algorithms in the task of bioethanol plant investment planning, the overall suitability of this type of technique has been tested. Moreover, the results allow us to determine which of the preceding algorithms is the most efficient in the given task.