• Energy Research

This paper focuses on the study of the effect of electrolysis on activated sludge in a microbial electrolysis cell (MEC) under the anaerobic digestion of poultry manure. This study was conducted using a bioreactor design with and without electrodes (conventional condition). Measurements of pH, redox potential (ORP), and total dissolved solids were carried out, as was the microscopy of activated sludge during treatment and gasometry. There was an increase in the yields of CH4 and CO2 compared to conventional conditions. Thus, on the 14th day, there was an increase in the CH4 yield to 35.1% compared with the conventional conditions—31.6%—as well as in the CO2 yield to 53.5% compared with the cell without electrodes—37.7%. Visually, the microscopy of anaerobic activated sludge showed changes in the aggregation process itself, with the formation of cells of clusters of microorganism colonies with branches of a delineated shape. ORP fluctuations were related to the process of the dissociation into ions during the passage of an electric current through the electrodes, and were observed before and after the inclusion of a current into the system. A model of the effect of electrolysis during anaerobic digestion was developed, taking into account the influencing factors on the condition of the activated sludge.

Energy can be obtained by pyrolysis of organic wastes, and the solid residue of pyrolysis (biochar) can be used as an adsorbent for the treatment of various types of wastewater. Although soil washing can effectively remediate metal-contaminated soils, it can generate significant amounts of soil washing wastewater (SWW). This study investigated the effectiveness of using activated carbon and various biochars to treat SWW from the remediation of soil heavily contaminated with cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) with soluble humic substances (SHS) from municipal sewage sludge. Willow biochar (BW), plant biomass biochar (BPB), coconut shell biochar (BCH), and Norit SX2 activated carbon (ACN) were tested at different dosages (12.5–100 g/L) and adsorption times (30–1440 min) for the treatment of SWW. At 100 g/L dosage, biochar removed Cd, Cu, Ni, Pb, and Zn with 56–83%, 32–41%, 18–42%, 75–83%, and 44–83% efficiency, respectively, while ACN removed them with 87–95% efficiency. Only BW and ACN removed soluble organics with efficiencies of 49% and 94%, respectively, at the highest dosage. Adsorption of metals and soluble organics was mainly controlled by physisorption and chemisorption. Diffusion of metals and soluble organics into the different pore sizes was not the most important rate-limiting step. ACN and BW had better structural properties and treated SWW most effectively. BPB and BCH removed metals but not soluble organics, which could be beneficial for SHS recycling.

This paper summarizes the physical and chemical properties of sediments dredged from the small water reservoir Klusov (Slovakia) and their potential use as a secondary raw material in concrete production. The effects of the sediment addition on the concrete technological properties (compressive and flexural strength, freeze–thaw resistance) have been determined. Concrete specimens were pressed and cured for 2, 7, 28 and 365 days. Results show that progress of compressive strength of concrete mixture, prepared from 20 wt% natural aggregate replacement by coarse-grained sediments, was similar to the control concrete mixture. Specimens containing fine-grained sediment as a cement replacement, at a 40:60 sediment/cement weight ratio, achieved compressive strength values below 35 % in comparison to that of the previous mixtures. The flexural strengths of concrete specimen prepared as a natural aggregate replacement were higher when compared to the reference mixture at all ages of hardening and have achieved a value of 6.59 MPa after 365 days of curing. Flexural strengths of the other samples were at about 4.0 MPa. All tested concrete specimens resistant to freeze–thaw attack achieved frost resistance coefficient values above 0.85 according to the standard requirements. The weight loss values after 50 cycles of freeze–thaw testing were in range from 1.33 to 2.5 % and hence the standard requirement of maximum 5 % weight loss was also fulfilled. The results of the frost resistance coefficient and the weight loss after 50 cycles of freeze–thaw testing show that the tested concrete specimens meet the standard requirements for frost-resistant concrete class XF2.

This research was realized in order to determine and analyze selected heavy metals present in sediment samples from Smolnik creek (Slovakia). The creek is permanently contaminated by acid mine drainage (AMD) from the Pech shaft with average annual flow rates of 6.5 L/s and pH ≈ 4, which acidifies and contaminates surface water and sediment with heavy metals. The pH decreases due to the mixture of AMD with surface water and is followed by metal precipitation and sedimentation in the (aquatic) environment. Potential ecological risk indexes were used to study the pollution status of heavy metals, Cu, Zn, As, and Pb, in sediment and assess their potential ecological risk to the environment. The heavy metals under investigation in the sediment reflected a low ecological risk for both sediment samples (S1 = 68.43; S2 = 53.47). The degree of sediment contamination in the Smolnik creek (Slovakia), for Fe, Mn, Al, Cu, Zn, As, and Pb, has been evaluated using an Enrichment factor, Pollution load index, Geo-accumulation index, single-factor index analysis, Neremo index comprehensive evaluation method, and metal pollution index to compare the total content of metals at the different sampling stations. The high PLI values 1.61 (S1) and 1.54 (S2) indicate strong signs of pollution deterioration. The results from Neremo index comprehensive evaluation method indicate that the S1 location is heavily polluted (3.12) and S2 is moderately polluted (2.95). These results confirmed the fact that the quality of sediment improves with the distance from source of contamination.

This study aims to evaluate the process of biogas production from the droppings of Clarias gariepinus under intensification of methanogenesis using electrolysis pretreatment and electro-fermentation in comparison with the addition of stimulating substances (humates and zeolites). For the realization of a series of experiments, laboratory installations of electrolysis and electro-fermentation were developed. The following parameters were monitored: biogas composition, chemical oxygen demand, redox potential, hydrogen potential, nitrates, ammonia–ammonium, and nitrites. A taxonomic classification and review of the metabolic pathways were performed using the KEGG, MetaCyc, and EzTaxon databases. The stimulation of biomethanogenesis in the utilization of catfish droppings by the introduction of additional electron donors—exogenous hydrogen (electro-fermentation)—was confirmed. The electro-fermentation process released 4.3 times more methane compared to conventional conditions and stimulant additives and released 1.7 times more with electrolysis pretreatment. The main metabolic pathways of electron acceptor recruitment using bioinformatic databases are highlighted, and models of CO2 transformation involving exogenous hydrogen along the chain of metabolic reactions of methanogenesis are generated. The summary model of metabolic pathways of methanogenesis are also proposed. Based on the results of the present and previous studies, two technological solutions are proposed to implement the process of anaerobic treatment intensification of excreta of the clariid catfish. Additional studies should include the optimization of the operation mode of electro-fermentation and electrolysis pretreatment of the substrate during the aquacultivation process.

To meet the needs of Egypt’s rising population, more land must be cultivated. Land evaluation is vital to achieving sustainable agricultural production. To determine the soil capability in the northeast Nile Delta region of Egypt, the present study introduces a new form of integration between the Agriculture Land Evaluation System (ALES Arid) model and the machine learning (ML) approach. The soil capability indicators required for the ALES Arid model were determined for the 47 collected soil profiles covering the study area. These indicators include soil pH, soil salinity, the sodium adsorption ratio (SAR), the exchangeable sodium percentage (ESP), the organic matter (OM) content, the calcium carbonate (CaCO3) content, the gypsum content, the clay percentage, and the slope. The ALES Arid model was run using these indicators, and soil capability indexes were obtained. Using GIS, these indexes helped to classify the study area into four capability classes, ranging from good to very poor soils. To predict the soil capability, three machine learning algorithms named traditional RVFL, sine cosine algorithm (SCA), and AFO were also applied to the same soil criteria. The developed ML method aims to enhance the prediction of soil capability. This method depends on improving the performance of Random Vector Functional Link (RVFL) using an optimization technique named Aptenodytes Forsteri Optimization (AFO). The operators of AFO were used to determine the best parameters of RVFL since traditional RVFL is sensitive to parameters. To assess the performance of the developed AFO-RVFL method, a set of real collected data was used. The experimental results illustrate the high efficacy of AFO-RVFL in the spatial prediction of soil capability. The correlations found in this study are critical for understanding the overall techniques for predicting soil capability.