Siberian Federal University

Issue

Journal of Siberian Federal University. Biology. 2018 11 (2)

Authors

Bartsev, Sergey I.; Beliy, Alexander V.; Sarangova, Antonina B.

Contact information

Bartsev, Sergey I.: Institute of Biophysics SB RAS FRC “Krasnoyarsk Science Center SB RAS” 50/50 Akademgorodok, Krasnoyarsk, 660036, Russia; Siberian Federal University 79 Svobodny, Krasnoyarsk, 660041, Russia; ; Beliy, Alexander V.: JSC Polyus Krasnoyarsk 37 Tsimlyanskaya Str., Krasnoyarsk, 660048, Russia; Sarangova, Antonina B.: Siberian Federal University 79 Svobodny, Krasnoyarsk, 660041, Russia

Keywords

bioleaching prediction; neural network analyses of biotechnological processes

Abstract

Problems related to the control of complex biotechnological processes were considered on the example of biooxidation of refractory gold-arsenic sulphide concentrates for the subsequent gold recovery. Two possible approaches to the problem were considered: a) building “mechanistic” mathematical model and b) applying neural network model. An attempt to construct a mixed mechanistic-phenomenological model using various combinations of formulas given in literature and general description of bioleaching processes has given not satisfactory result. The models were able to describe only the general properties and trends of the process. Neural network analysis of time series of the bioleaching process has revealed dependences between the process, control parameters, and feed composition. Obtained 10% level of the forecast error (MAPE) is quite satisfactory if compare with the forecasts of any natural ecosystem. It can be argued that the relatively low complexity of neural network indicates the possibility of developing a fairly simple mechanistic model of the bioleaching process

Pages

181-189

Paper at repository of SibFU

https://elib.sfu-kras.ru/handle/2311/71721

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).