Taking into consideration the fact that used oils are considered hazardous and extremely toxic, their subsequent use creates a real threat to the environment. The only effective measure for protecting the natural environment from used oil contamination is collection and controlled utilization. Only appropriate used oil management can guarantee minimal environmental contamination and prevent its irreversible degradation. The aim of our study was to determine the content of select heavy metal elements in used engine oils originating from agricultural tractors and agricultural machines of varying mileage. The tests were carried out by using the HD XRF method. The researched samples showed a high content of zinc remaining at 726- 1,389 mg·kg^-1. The contents of molybdenum, iron, and copper also were high, at Mo 360-689 mg·kg^-1, Fe 16.5- 267 mg·kg^-1, and Cu 4.63-76.87 mg·kg^-1. Low nickel, manganese, chromium, and lead contents were found in the ranges: Ni 0.14-0.75 mg·kg^-1, Mn 0.41-4.27 mg·kg^-1, Cr 0.36-10.66 mg·kg^-1, and Pb 0.9-13.71 mg·kg^-1. Mercury and cadmium were not found in the tested oils.

This paper presents the results of the evaluation of the energy potential of waste from the process of drying corn grain in the form of corn cobs, damaged grains, corn grain husks, and mixtures of starting materials. A technical and elementary analysis was performed for the biomass under investigation. The elemental composition of ash and the tendencies for slagging and boiler slagging were determined, and the emission factors were estimated based on the elemental analysis performed. The tests showed the highest calorific value among the starting materials for corn cobs (CCs) (14.94 MJ·kg⁻¹) and for the mixture of corn cobs with corn husk (CC–CH) (13.70 MJ·kg⁻¹). The estimated emission factors were within ranges of 38.26–63.26 kg·Mg⁻¹ for CO, 936–1549 kg·Mg⁻¹ for CO₂, 0.85–4.32 kg·Mg⁻¹ for NO_x, 0.91–1.03 kg·Mg⁻¹ for SO₂, and 3.88–54.31 kg·Mg⁻¹ for dust. The research showed that the creation of mixtures from starting materials leads to materials with lower potential for negative environmental impact as well as a reduced risk of slagging and fouling of biomass boilers. However, taking into account all the parameters determined for the biomass under study, the highest energy potential was characteristic for corn cobs and the mixture of corn cobs with corn husk.

In this paper we address automatic vehicle and engine identification based on audio information. Such data depend on many factors, including vehicle type, tires, speed and its change, as well as road type. In our previous research we designed a feature set for selected vehicle classes, discriminating pairs of classes. Later, we decided to expand the feature vector and find the best feature set (mainly based on spectral descriptors), possibly representative for each investigated vehicle category, which can be applied to a bigger data set, with more classes. The experiments were performed first on on-road recordings, and then continued with test bench (dyno) recordings. The paper also shows problems related to vehicles classification, which is detailed in official documents by national authority for issues related to the national road system, but simplified for automatic identification purposes. Experiments on audio-based vehicle type and engine type identification are presented and conclusions are shown.

This article is a study of Hydrotreated Vegetable Oil and Butanol Fuel blends, which are mixed in three different proportions (HVOB5, HVOB10 and HVOB20), and the comparison of their combustion (in-cylinder pressure, pressure rise and ROHR), performance (fuel consumption, BSFC and BTE) and emission (CO₂, NO_X, HC and Smoke) characteristics with those of fossil diesel fuel. In the wake of finding an alternative fuel that requires little to zero modifications to the existing IC engines, it is necessary to account for the necessity of matching the efficiency of conventional fuels as well as greatly reducing its exhaust emissions. As a result of transesterification, HVO is found to have better stability and higher CN compared to other biofuels. It is termed a “renewable diesel” due to its ability to reduce emissions while maintaining efficiency. HVO as a fuel has higher cost efficiency, and for a more stable oxygen content in the fuel, an alcohol substitute is needed. Butanol, which has a considerable advantage over other alcohols due to its higher density, viscosity and CN, is selected. HVOB5 and HVOB10 are found to match diesel fuel in terms of fuel consumption while having a ~1% lesser efficiency. In terms of emissions, all the fuel mixtures including HVO100 are found to have ~4–5% lesser CO₂, ~10–15% lesser NO_X and a ~25–45% reduction in smoke levels.

In this paper, we focus on detection of speed changes from audio data, representing recordings of cars passing a microphone placed near the road. The goal of this work is to observe the behavior of drivers near control points, in order to check whether their driving is safe both when approaching the speed camera and after passing it. The audio data were recorded in controlled conditions, and they are publicly available for downloading. They represent one of three classes: car accelerating, decelerating, or maintaining constant speed. We used SVM, random forests, and artificial neural networks as classifiers, as well as the time series based approach. We also tested several approaches to audio data representation, namely: average values of basic audio features within the analyzed time segment, parametric description of the time evolution of these features, and parametric description of curves (lines) in the spectrogram. Additionally, the combinations of these representations were used in classification experiments. As a final step, we constructed an ensemble classifier, consisting of the best models. The proposed solution achieved an accuracy of almost 95%, without mistaking acceleration with deceleration, and very rare mistakes between stable speed and speed changes. The outcomes of this work can become a basis for campaigns aiming at improving traffic safety.

The aim of this paper is to compare the results of kinematic viscosity of lubricating oils measurements at 40 °C, obtained with three different rapid evaluation devices, and the standardized method using an Ubbelohde Capillary viscometer. The following instruments were selected to measure: a mid-FTIR spectrophotometer, a microchannel viscometer, and a Stabinger viscometer. The study material comprised 42 fresh engine oils, all of which are commercially available. The main data analysis tools used in the study were multiple regression, Mahala Nobis distance, post-hoc analysis, and the Wilcoxon signed-rank test with the Bonferroni correction. Consistent outcomes were obtained for the Stabinger viscometer only, whereas the microchannel viscometer and the mid-FTIR spectrophotometer were not as precise as the reference method. It was also found that the results obtained with the use of the mid-FTIR spectrophotometer were burdened with a very large measurement error. Therefore, a very careful approach is suggested when choosing these instruments. The study fills an important gap in empirical research in the context of the reliability of measurement results obtained using various research techniques.