The purpose of the flotation process control is optimization the concentrate grade and recovery, while maximizing profits. Consequently, the research into modeling and control of this process has always been an important area in control engineering practice. This paper presents the results of development and validation the predictive models, based on the ANFIS hybrid system. Models predict the values of copper concentrate and tailings grade as well as copper recovery in the flotation plant "Veliki Krivelj". The copper content in the feed ore, collector consumption in the rough flotation stage and consumption of frother, were selected as the independent variables. Other technical and technological parameters, relevant for the process of flotation concentration were considered constant. The results of the models validation have showed that the models provide the good predictions of changes in the copper concentrate grade, while the predictions of changes in the copper recovery and tailings grade are somewhat poorer.

Reducing the costs of repairing concrete structures damaged due to the appearance of cracks and reducing the number of people involved in the process of their repair is the subject of a multitude of experimental studies. Special emphasis should be placed on research involving industrial by-products, the disposal of which has a negative environmental impact, as is the case in the research presented in this paper. The basic idea was to prepare a mortar with added granulated blast furnace slag from Smederevo Steel Mill and then treat artificialy produced cracks with a Sporosarcina pasteurii DSM 33 suspension under the conditions of sterile demineralized and water from Danube river in order to simulate natural conditions. The results show a bio-stimulated healing efficiency of 32.02% in sterile demineralized water and 42.74% in Danube water already after 14 days. The SEM images clearly show calcium carbonate crystals as the main compound that has started to fill the crack, and the crystals are much more developed under the Danube water conditions. As a special type of research, microscopic images of cracks were classified into those with and without the presence of bacterial culture. By applying convolutional neural networks (ResNet 50), the classification success rate was 91.55%.

Reducing the costs of repairing concrete structures damaged due to the appearance of cracks and reducing the number of people involved in the process of their repair is the subject of a multitude of experimental studies. Special emphasis should be placed on research involving industrial by-products, the disposal of which has a negative environmental impact, as is the case in the research presented in this paper. The basic idea was to prepare a mortar with added granulated blast furnace slag from Smederevo Steel Mill and then treat artificially produced cracks with a Sporosarcina pasteurii DSM 33 suspension under the conditions of both sterile demineralized water and water from the Danube river in order to simulate natural conditions. The results show a bio-stimulated healing efficiency of 32.02% in sterile demineralized water and 42.74% in Danube river water already after 14 days. The SEM images clearly show calcium carbonate crystals as the main compound that has started to fill the crack, and the crystals are much more developed under the Danube river water conditions. As a special type of research, microscopic images of cracks were classified into those with and without the presence of bacterial culture. By applying convolutional neural networks (ResNet 50), the classification success rate was 91.55%.