Due to the key role of the biological decomposition process of organic compounds in wastewater treatment, a very important thing is appropriate aeration of activated sludge, because microorganisms have to be supplied with an appropriate amount of oxygen. Aeration is one of the most energy-consuming processes in the conventional activated sludge systems of wastewater treatment technology (may consume from 50% to 90% of electricity used by a plant), which makes it the most cost-generating process incurred by treatment plants. The paper presents the construction of aeration systems, their classification as well as parameters and factors that significantly affect the aeration process e.g., oxygen transfer efficiency, diffuser fouling, methods of dealing with diffuser fouling, diffuser selection. Additionally, there are briefly presented “smart control” systems in wastewater treatment and effect of application control strategy based on Supervisory Control and Data Acquisition system connected with the decrease in the energy consumption for aeration of bioreactors with activated sludge. It is noted that before the process is optimized, the system should be equipped with suitable metering devices. Only when relevant data is available, the improvements can be carried out. However, it’s important, that the operator should regularly maintain good condition and high efficiency of diffusers.

The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the classification of the various stages of the wastewater treatment process. To evaluate the multidimensional measurement derived from the gas sensors array, dimensionality reduction was performed using the t-SNE method, which (unlike the commonly used PCA method) preserves the local structure of the data by minimizing the Kullback-Leibler divergence between the two distributions with respect to the location of points on the map. The k-median method was used to evaluate the discretization potential of the collected multidimensional data. It showed that observations from different stages of the wastewater treatment process have varying chemical fingerprints. In the final stage of data analysis, a supervised machine learning method, in the form of a random forest, was used to classify observations based on the measurements from the sensors array. The quality of the resulting model was assessed based on several measures commonly used in classification tasks. All the measures used confirmed that the classification model perfectly assigned classes to the observations from the test set, which also confirmed the absence of model overfitting.

In this review, the problem of microbiological infestation of buildings was discussed. The techniques of detection were described as well, with special attention drawn to the rapid-early detection methods based on gas sensor arrays. The physical and chemical conditions of the building environment conducive to the development of microorganisms and the technical conditions influencing the problem of microbiological infestation were investigated. Additionally, the harmful effects on human health caused by the microbiological contamination were discussed, with a short review of particular groups of microorganisms causing sick building syndrome. Among the detection techniques, the traditional microbiological techniques as well as the molecular and chemical methods were presented. Different designs of the gas sensor arrays together with the various techniques of analyzing the received multidimensional signal were described, analyzed, and compared in detail.

This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; thus, the samples could be classified in terms of their similarity and the analyzed gas-fingerprint could be related to the pollution level expressed by physical and biochemical indicators. Principal component analysis was employed for dimensionality reduction, and cluster analysis for grouping observation purposes. Supervised learning techniques confirmed that the obtained data were applicable for the classification of wastewater at different stages of the purification process.

This paper investigates the physical and mechanical properties of mortars modified with waste polyurethane foam (WPF) and the influence of hydrophobising agents on the physical characteristics. The sand was substituted with WPF (2-4 mm grain size) in the mortars which consisted of 5, 10 and 15% of foam, respectively. The contact angle of lightweight mortars was determined (θ_w) in the function of time, prior to and after the frost resistance test. The surface free energy characterizing the wettability and adhesion of mortars under normal conditions and following frost erosion was calculated with Neumann’s method based on the obtained data. The structure of mortars, adhesion of lightweight aggregate to cement paste and the structure of thin hydrophobic film were demonstrated by means of scanning electron microscopy. The mortars subjected to hydrophobisation process revealed a slight mass change caused by freezing and thawing processes: 0.1% for methyl silicone resin, 2.1% for alkyl-alkoxy-silane, and 9.2% for the samples which were not hydrophobized. On average, the contact angle of the standard mortars was 3 times lower than the one of hydrophobic material. The best results illustrating the efficiency of hydrophobisation were obtained for methyl silicone resin.

Introduction and objective: The article analyzes the content of heavy metals and standard physical as well as chemical pollution indicators in different types of sediments from stormwater, combined sewer and sanitary sewer systems. Material and methods: Nickel, lead, chromium, copper, zinc...