Journal
FUEL PROCESSING TECHNOLOGY
Volume 125, Issue -, Pages 79-85Publisher
ELSEVIER
DOI: 10.1016/j.fuproc.2014.03.023
Keywords
Desulphurization; Ash removal; Bitumen; Flotation; Leaching; Artificial neural network
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Flotation and leaching methods were used to remove ash and sulfur from bitumen by sulfuric acid. The bitumen samples had sulfur content of 9.6% (6.74% in the pyrite sulfur form) and 30% ash. All the experiments were done under aeration rate of 4 L/min using pine oil and gasoline as frother and collector agents, respectively. The factors studied were including the amounts of collector and frother agents, pH, solid weight percentage in the pulp, stirrer speed, and particle size. The bitumen samples with dimensions less than 0.5 mm were crushed. The flotation experiments were performed in a 3-L Denver laboratory flotation cell to ease the ash and sulfur removal. The optimum condition for plant operation were; foaming (50 gr/t), collector (1 kg/t), impeller speed of 1200 rpm, pH = 7, pulp containing 5% of solid, particle size of 100 mesh, and flotation time of 3 min. In these circumstances, 52.9% of pyrite sulfur (e.g.; 36.45% of total sulfur) and 43% of ash were removed. With the approach of leaching using sulfuric acid, the organic and pyrite sulfur removal were 7% and 13%, respectively. Combination of these two methods (in optimal conditions), removed up to 47% of the total sulfur and 61% of ash through bitumen sample. In the next step of study Artificial Neural Networks (ANN) was employed to model the ash and sulfur removals data obtained by flotation method. A network consisting of two layers of six and nine neurons in the hidden layer were considered. Meanwhile learning algorithm Levenberg-Marquardt (LM) was used. In neural network models Tansig and Purelin, transfer functions for hidden and output layers were applied, respectively. Very low error in the network estimation confirmed validity of the obtained networks for further analysis and optimization. Moreover, process optimization were carried out by using ANN to predict the best operating conditions, which resulted in the maximum percentage of ash and pyrite sulfur removal from bitumen. The maximum percentage of ash was estimated by ANN to be 42.39% under the following operational conditions; foaming amount of 50.5gr/t, the collector amount of 1.12 kg/t, impeller speed of 1200 rpm, pH = 7.5, pulp equal to 5% of solid, particle size of 110 mesh, and flotation time of 3 min. The maximum percentage of pyrite sulfur removal was estimated by ANN to be 52.15% under the following operational conditions; foaming amount of 50gr/t, the collector amount of 1.4 kg/t, impeller speed of 1200 rpm, pH = 7, pulp equal to 5% of solid, particle size of 100 mesh, and flotation time of 3 min. (C) 2014 Elsevier B.V. All rights reserved.
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