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The paper presents the resistances to the formation of a bale of recyclable cardboard, in the ten stages of its formation, up to binding, as well as on each stage separately, for the pressing phase and for the expansion phase, until the start of a new cycle, including the mass variation of the bale in formation. It also presents the energy accumulated in the bale for each phase of its formation, and the total energy required for its formation. The presented data can be used both by the builders of balers for pressing and baling recyclable waste, and by their users to make the best settings.

The calculations were done for a wastewater treatment plant that had mechanical, biological, and sludge processing stages and served a population of about 11,100 people. Domestic wastewater, storm water, and industrial water collected from businesses that release into the sewage system are all treated at the treatment plant. The STAS 6953-81 method was used in this research to determine the total suspended solids content. The purification plant's efficiency in removing these substances was assessed using measurements taken from both the influent and effluent. The quantities of coarse material (by weighing) retained from the sewage treatment plant using the two types of screens were also determined. It was observed that the efficiency of the treatment plant was over 98%.

The paper presents several constructive variants of power systems and makes a comparative analysis of them, both constructively and functionally. The kinematic analysis of a bar and lever mechanism is also realized, which is used in a wide range of compaction by translation. Elucidation of the movement of the working organs of these mechanisms is necessary for a good understanding of their operation, but especially for redesigning and improving their functional parameters for a low loss and low energy operation.

Briquetting is considered one of the pre-treatment methods available to produce raw materials of uniform size and moisture content that are easy to process, transport, and store. The quality of briquettes in terms of density and strength depends on the physical and chemical properties of the raw material and the briquetting conditions. However, determining briquette quality is difficult, very costly, and requires long laboratory studies. In this paper, an easy, inexpensive, and fast methodology based on machine learning for the determination of quality parameters of briquette samples is presented. Compressive resistance, one of the most important briquette quality parameters, was estimated by machine learning methods, considering particle size, material moisture, applied pressure value, briquette density, shatter index, and tumbler index. Extra Trees, Random Forest, and Light Gradient Boosting regression models were used. The best estimate is seen in the Extra Trees regression model. The R2 and MAPE values are 0.76 and 0.0799, respectively.