Waste water in the galvanic process contains high concentrations of heavy metals that pose a direct danger to humans and the environment. Conventional methods for their removal are quite expensive and generate a large amount of waste. The development of new and improvement of existing methods for the removal of heavy metals from galvanic wastewater are the subject of many studies. Compared to other purification methods, the adsorption is becoming an increasingly popular method of wastewater purification, especially if the adsorbent is cheap, easily available and does not require any other treatment before use. Therefore, the aim of the work was to investigate the possibility of using natural bentonite for the removal of heavy metal ions from multi-component water systems of the galvanic industry. For this purpose, the physico-chemical characterization of natural bentonite was performed, and then the influence of pH value, time and temperature on the adsorption efficiency was examined. The results of adsorption showed that natural bentonite can be used as an adsorbent for the removal of heavy metal ions from waste galvanic waters, and that at pH 5 it achieves the maximum removal efficiency for Cu(II):Cr(III):Ni(II) ions in the percentage ratio 100 : 99.990 : 99.998. The results showed that the highest removal efficiency for Cu (II) ions was achieved in the first 10 minutes, and 20 minutes for Cr (III) and Ni (II) ions. The maximum efficiency of Cu (II) removal was achieved at all temperatures, while for Cr (III) 99.99% and Ni (II) 100% maximum efficiency was achieved at 35°C, which indicates that the adsorption process is endothermic. The experimental results of the adsorption of Cu (II) metal ions are in good agreement with the Langmuir and Freundlich theoretical models, while for Cr (III) and Ni (II) ions they are in better agreement with the Langmuir adsorption model.

Presently, the main cause of pollution of natural water resources is heavy metal ions. The removal of metal ions such as nickel (Ni2+) and cadmium (Cd2+) has been given considerable attention due to their health and environmental risks. In this regard, for wastewater treatment containing heavy metal ions, graphene oxide (GO) nanocomposites with metal oxide nanoparticles (NPs) attained significant importance. In this study, graphene oxide stacked with copper oxide nanocomposites (GO/CuO-NCs) were synthesized and characterized by Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray (EDX), and atomic force microscopy (AFM) analytical procedures. The prepared GO/CuO-NCs were applied for the removal of Ni2+ and Cd2+ ions from a binary metal ion system in batch and continuous experiments. The obtained results revealed that GO/CuO-NCs exhibited the highest removal efficiencies of Ni2+ (89.60% ± 2.12%) and Cd2+ (97.10% ± 1.91%) at the optimum values of pH: 8, dose: 0.25 g, contact time: 60 min, and at 50 ppm initial metal ion concentration in a batch study. However, 4 mL/min flow rate, 50 ppm initial concentration, and 2 cm bed height were proved to be the suitable conditions for metal ion adsorption in the column study. The kinetic adsorption data exhibited the best fitting with the pseudo-second-order model. The adsorption isotherm provided the best-fitting data in the Langmuir isotherm model. This study suggested that the GO/CuO nanocomposites have proved to be efficient adsorbents for Ni2+ and Cd2+ ions from a binary metal system.

In this research, a physicochemical analysis of the industrial wastewater from a factory that produces maleic anhydride was performed. Based on the conducted analysis (pH, electrical conductivity, density of the liquid phase, boiling point of the waste suspension, chemical as well as biological oxygen demand, and dry matter), it can be concluded that the waste stream obtained at the outlet pipe from the plant resulting from the production of maleic anhydride requires appropriate treatments. Some of the parameters measured, e.g. pH (0.97±0.06), boiling point (106.8±1.3°C) and acidity, indicate the presence of organic acids such as fumaric and maleic acids, which are formed during the production of maleic anhydride. The possibility of extracting crystals by adding urea and thiourea followed by forced cooling in a heat exchanger was investigated. The most effective method was the addition of thiourea when the most significant amount of crystals was obtained, namely 17.29 wt%. The addition of thiourea in combination with forced cooling greatly facilitates the process of separating the solid and liquid phases of the waste suspension, which could later be adequately treated by physical, chemical or biological methods.

In this research, the results of bentonite characterization (pH value of bentonite suspension, point of zero charge, cation exchange capacity,  SEM, XRF, DTG) are presented. The results of lead (II) removal efficiency at initial lead (II) concentrations of 200, 300 and 400 mg/L, and biosorbent dosage of 1 gram in 50 ml of lead(II) solution,  are also presented, as well as the values of the Freundlich and Langmuir constants from the Freundlich and Langmuir adsorption isotherms. The obtained results showed that removal efficiency is high for all three examined initial lead (II) concentrations, and it is above 99%. The lead (II) removal efficiency slightly decreases with an increase in initial lead concentration. Experimental data obtained from adsorption experiment with contact time of 2.5 minutes, stirring rate 100 rpm, temperature 250C and pH value 5 are better fitted with the linearized Langmuir equation isotherm, giving an R2 value closest to unity (0.9994), than to linearized Freundlich equation (0.9886).

Sludge generated in wastewater treatment processes must be treated in an adequate manner, and therefore disposed of in an environmentally friendly manner. The biggest obstacle to the efficient use of sludge is the high water content, therefore the development of methods to accelerate the sludge dewatering process is particularly important. For the purposes of the research, the waste sludge created in the process of purifying ammoniaphenol wastewater in the GIKIL factory was used. Sludge conditioning was performed by adding a commercial flocculant (0.1 % solution) in combination with pyrophyllite, kaolin clay and shredded cardboard. According to earlier research, the volume of waste sludge with the addition of a suitable flocculant could be reduced by more than 5 times compared to the initial amount, leaving behind a large amount of separated water that can be returned to the process. The results show that the addition of commercial flocculant in the amount of 0.8 % gives the best results (reduction of sludge volume by 78.8 %). A lower percentage was found in samples with a mixture of flocculants with pyrophyllite (78 %) and kaolin (77.6 %), while the combination of flocculants with waste cardboard was ineffective (64.4 %). Addition of flocculant to waste sludge resulted in a decrease in specific filtration resistance (1.15x107 s2/g). A decrease in specific resistance was also observed in flocculant/kaolin clay (0.8x107 s2/g) and flocculant/cardboard (1.09x107 s2/g) samples. Sludge conditioning also resulted in a reduction of suspended solids in the neonate compared to settled raw sludge without additives.

In this paper a mathematical model for the soda ash drying process in a pneumatic dryer was presented. The model presents a macroscopic aspect of the drying process, for a two-phase, gas-solid system. The model is based on mass and heat transfer between the gas phase and the particle, movement of air and particles through the system, and geometric characteristics of the drying system (fan, air heater, pneumatic dryer, and cyclone). The effects of the process parameters, such as airflow, inlet air temperature, and relative humidity, temperature at the inlet of the dryer, etc., have been studied by solving the model. Also, the model was tested for different values of the capacity of wet soda and different values of the operating parameters of the heating medium. The model was implemented in MATLAB and solved with a nonlinear equations solver. Data obtained by the model were compared with industrial pneumatic dryer data for drying wet soda ash particles with good agreement.

In this paper, the optimization of process parameters for the composting process in a batch reactor was performed. Optimized process parameters are airflow and the initial moisture content. The objective function for the optimization problem the maximization of degradation of organic matter is selected. The mathematical relation for the objective function was derived from experimental data and data obtained by numerical simulations. The experimental data of variables were fitted using the multiple regression analysis and found it was statistically significant.Experimental data include a wide range of values for airflow and initial moisture content, which give a maximum degradation of organic matter of 50.1% for a process that lasts (the longest) continuously for 23 days. The optimization problem was solved by applying nonlinear optimization within the Matlab programming package. The obtained optimum values of airflow and moisture content are 0.296 m3/(kgomh) and 61.6%, respectively. Verification of the process for optimized process parameters was performed in the pilot batch reactor with controlled conditions (SD + 5% of the maximum conversion of organic matter).

: In this paper, a local sensitivity analysis was performed using one at time technique (OAT) on the parameters of the mathematical model for the composting process. An integrated mathematical model for composting process was used, in which kinetic parameters and the reaction order was estimated. The values of the absolute and relative sensitivity of the specified parameters are calculated. The following dynamic variables were selected as the objectives functions for sensitivity analysis: the mass of organic matter at the end of the process, the minimum amount of oxygen, the maximum amount of carbon dioxide and the maximum substrate temperature. The sensitivity analysis showed that the variations of the parameters mostly affect the amount of carbon dioxide, and at least the substrate temperature, and that the most sensitive parameter is the reaction order. ANOVA analysis (one-way and two-way) showed a statistically significant difference between experimental data.