The tanning industry generates significant amounts of solid waste and post-production wastewater, which should be managed in accordance with the principles of the circular economy. Waste is generated at various technological stages of production. This comprises mainly solid waste and leachate. A comprehensive solution to the problem of disposal of this waste is very important from economic and environmental points of view. This work presents research for a technological production line designed for the comprehensive processing of post-production residues. In the present paper, the authors present an analysis related to the possibility of processing leather flesh side (mizdra) as a substrate for methane fermentation. The study showed an increased biogas production from solid waste after enzymatic hydrolysis up to 248 Nm³/Mg. Preliminary research on the system designed for pre-treatment of sewage from the technological line is also presented. The study showed a COD reduction of more than 30%. The possibility of energy management of the solid fraction directly from the processes carried out is demonstrated.

This paper presents the possibility of the energetic utilization of biowaste in the form of lignified one-year shoots from the cultivation of grapevines of the Seyval Blanc (WSBL), Solaris (WSOL), Regent (WREG) and Rondo (WRON) varieties, grown in temperate climate zones. A technical analysis, an elemental analysis and the determination of the highest heat value and lowest heat value were performed to define the quality parameters of waste as fuel. In addition, the emission factors of SO₂, NO_x, CO, CO₂ and dust were estimated to demonstrate the impact of potential biowaste from combustion. Based on the stoichiometric equations, the exhaust gas composition, the theoretical oxygen demand and the total flue gas volume were evaluated. The study showed that the material with the highest energy potential was WREG (LHV-16.19 MJ·kg⁻¹), with an ash content of 3.68%, while the lowest potential was found for WRON (LHV-15.88 MJ·kg⁻¹), with an ash content of 4.21%. The study showed that the use of the studied viticulture waste instead of hard coal could reduce CO emissions by 26–27%, CO₂ by 24–26%, NO_x by 55–56%, SO₂ by 96–97% and dust by 77–80%.

The torrefaction process represents a thermal conversion technique conducted at relatively low temperatures ranging between 200 to 300 °C. Its objective is to produce fuel with a higher energy density by decomposing the reactive portion of hemicellulose. In this study, the kinetics of mass loss during torrefaction were investigated for willow. The experiments were carried out under isothermal conditions using thermogravimetric analysis. Batch torrefaction reactor designs were conducted and explained in detail. Co-combustion of willow with hard coal (origin: Katowice mine) in different mass ratios (25% biomass + 75% coal, 50% biomass + 50% coal, and 75% biomass + 25% coal) was conducted in addition to raw biomass torrefaction. TG/MS analysis (a combination of thermogravimetric analysis with mass spectrometry analysis) was performed in the research. The optimal torrefaction conditions for willow were identified as an average temperature of 245 °C and a residence time of 14 min, resulting in the lowest mass loss (30.15%). However, it was noted that the composition of torgas, a by-product of torrefaction, presents challenges in providing a combustible gas with sufficient heat flux to meet the energy needs of the process. Prolonged residence times over 15 min and higher average temperatures above 250 °C lead to excessive energy losses from volatile torrefaction products, making them suboptimal for willow. On the other hand, the co-combustion of torrefied biomass with hard coal offers advantages in reduced sulfur emissions but can lead to increased NOx emissions when biomass with a higher nitrogen content is co-combusted in proportions exceeding 50% biomass. This paper summarizes findings related to optimizing torrefaction conditions, challenges in torgas composition, and the emissions implications of co-combustion.

Biogas plants are one of the most stable sources of renewable energy. Currently, there is a noticeable increase in the amount of post-production residues from agricultural production and agri-food processing (fruit and vegetable processing, fermentation, beet pulp, or lignocellulosic waste), which, can be used for biogas production after appropriate pretreatment. The aim of this study was to examine the possibility of using the biomass produced during the cultivation of grapes on a selected farm as a substrate for a biogas plant, taking into account the production process. The research was carried out in 2018–2020 in a vineyard located in the Sandomierz Upland in the south-eastern part of Poland. Own rooted vines were grown as a single continuous string with a trunk height of 40 cm and a length of one fixed arm approx. 0.9 m, on which six pivots were left every year after applying a short cut, from which 12–16 fruit shoots were derived, the so-called grapevines. Leaves were collected at random from three locations on the fruiting shoot, a total of 30 leaves in each replicate. Each sample consisted of 1/3 of the leaves collected at the bottom, 1/3 in the middle, and 1/3 at the top of the canopy. Leaf area was estimated with a model 3100 area meter on a sample of 30 leaves from each replicate. Both the quantity and quality of the obtained material as a substrate for methane fermentation were evaluated. Biogas yield tests in optimal conditions for mesophilic bacteria were conducted on three substrate samples referred to as ‘Regent’, ‘Seyval Blanc’, and ‘Solaris’. The yields of the tested material ranged from 51.0 to 59.0 Nm 3 biogas per Mg of biomass.

Solar energy is a promising and efficient source of electricity in countries with stable and high sunshine duration. However, in less favorable conditions, for example in continental, temperate climates, the process requires optimization to be cost-effective. This cannot be done without the support of appropriate mathematical and numerical methods. This work presents a procedure for the construction and optimization of an artificial neural network (ANN), along with an example of its practical application under the conditions mentioned above. In the study, data gathered from a photovoltaic system in 457 consecutive days were utilized. The data includes measurements of generated power, as well as meteorological records. The cascade-forward ANN was trained with a resilient backpropagation procedure and sum squared error as a performance function. The final ANN has two hidden layers with nine and six nodes. This resulted in a relative error of 10.78% and R² of 0.92–0.97 depending on the data sample. The case study was used to present an example of the potential application of the tool. This approach proved the real benefits of the optimization of energy consumption.