Combined heat and power generation is the simultaneous conversion of primary energy (in the form of fuel) in a technical system into useful thermal and mechanical energy (as the basis for the generation of electricity). This method of energy conversion offers a high degree of efficiency (i.e., very efficient conversion of fuel to useful energy). In the context of energy system transformation, combined heat and power (CHP) is a fundamental pillar and link between the volatile electricity market and the heat market, which enables better planning. This article presents an advanced model for the production of fuel mixtures based on landfill biogas in the context of energy use in CHP units. The search for optimal technological solutions in energy management requires specialized domain-specific knowledge which, using advanced algorithmic models, has the potential to become an essential element in real-time intelligent ICT systems. Numerical modeling makes it possible to build systems based on the knowledge of complex systems, processes, and equipment in a relatively short time. The focus was on analyzing the applicability of algorithmic models based on artificial intelligence implemented in the supervisory control systems (SCADA-type systems including Virtual SCADA) of technological processes in waste management systems. The novelty of the presented solution is the application of predictive diagnostic tools based on multithreaded polymorphic models, supporting making decisions that control complex technological processes and objects and solving the problem of optimal control for intelligent dynamic objects with a logical representation of knowledge about the process, the control object, and the control, for which the learning process consists of successive validation and updating of knowledge and using the results of this updating to determine control decisions.

Street sweeping waste (SSW) represents a heterogeneous urban material rich in road-deposited particles and associated contaminants, including heavy metals. Although SSW is generated systematically in all cities and constitutes a relevant indicator of diffuse urban pollution, research addressing its chemical composition, especially its temporal dynamics, remains limited. This study investigates the annual variability of total and leachable concentrations of heavy metals (Zn, Cu, Pb, Ni, Cr, and Cd) in SSW collected monthly over a one-year period in a medium-sized city. Total metal content was determined using aqua regia digestion, while leachability was assessed through standardized extraction tests. Statistical evaluation included correlation analysis, variability indices and assessment of metal mobility. Results confirm that both total and leachable metal concentrations exhibit distinct seasonal patterns, strongly influenced by traffic intensity, winter maintenance practices, and fluctuations in particle size distribution. Several metals demonstrated a high mobility factor, highlighting the potential environmental risk associated with their transport to urban soils and stormwater systems. The findings emphasize the necessity of incorporating temporal dynamics into SSW management strategies and demonstrate the importance of monitoring mobile metal fractions rather than relying solely on total concentrations. Due to the scarcity of annual-scale studies on SSW contamination, this research contributes novel insights into the environmental behavior of heavy metals in urban settings.