As a waste neutralization facility, the landfill is a kind of bioreactor producing landfill gas or (LFG) - biogas, which should be captured and neutralized for environmental reasons. One of the ways of its utilization is the combined production of heat and electrical energy in combined heat and...

Standard methods for assessing the environmental impact of waste management systems are needed to underpin the development and implementation of sustainable waste management practice. Life cycle as...

Landfill gas recovery and utilisation is a solution which reduces the adverse environmental impact of the landfill. Combined heat and power (CHP) generation improves the energy balance of the facility and enables the optimal management of energy generated from a renewable source. This article aims to analyse the operation of the CHP unit in two aspects, that is, in terms of energy generation efficiency and operational availability. Energy ratios were calculated and the analysis was based on the Weibull distribution in order to assess the CHP unit’s operational reliability to minimise costs and maximise energy production. The results of the investigations and analyses demonstrated an increase of the gas yield by 29.5%, an increase of energy production by approx. 42%, and the reduction of downtime by 28.2% from 2018 to 2022. Studies related to the efficiency and reliability of operation of the cogeneration unit showed an increase in all the main parameters analysed, which resulted in greater energy and operational efficiency. The research which has been conducted is a significant scientific contribution to the optimisation of the “waste-to-energy” process for cogeneration units with the capacity of up to 0.5 MW.

An attempt was made to estimate the annual production of CH₄ at a municipal waste landfill site in Poland. As a matter of fact, the extent of the unorganized emission of CH₄ from the landfill surface was approached based on the adopted mathematical model. The Ward agglomeration method for cluster analysis and the Pearson coefficient were employed to evaluate the distance-based similarity measure and to optimize methods for estimating methane emissions from a landfill as well as to verify the input parameters for the model. In order to calculate the content of biodegradable organic parts in the waste, morphological tests of the landfilled waste were performed. Physical quantities, measurements and the actual amount of the landfilled waste as well as the volume of CH₄ neutralized in a collective flare were implemented in the model, respectively. The model-based findings and experimental outcome demonstrated stable gas production in the landfill with a high CH₄ content. On the other hand, a rather low efficiency of the landfill passive degassing installation indicated the necessity to design and develop its active counterpart with the prospective application of the generated biogas for energy production in a cogeneration system.

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.

The paper presents advanced computational solutions for selected sectors in the context of the optimization of technology processes as an innovation and progress in improving energy efficiency of smart cities. The main emphasis was placed on the sectors of critical urban infrastructure, including in particular the use of algorithmic models based on artificial intelligence implemented in supervisory control systems (SCADA-type, including Virtual SCADA) of technological processes involving the sewage treatment systems (including in particular wastewater treatment systems) and waste management systems. The novelty of the presented solution involves the use of predictive diagnostic tools, based on multi-threaded polymorphic models supporting decision making processes during the control of a complex technological process and objects of distributed network systems (smart water grid, smart sewage system, smart waste management system) and solving problems of optimal control for smart dynamic objects with logical representation of knowledge about the process, the control object and the control itself, for which the learning process consists of successive validation and updating of knowledge and the use of the results of this updating to make control decisions. The advantage of the proposed solution in relation to the existing ones lies in the use of advanced models of predictive diagnostics, validation and reconstruction of data, implemented in functional tools, allowing the stabilization of the work of technological objects through the use of FTC technology (fault tolerant control) and soft sensors, predictive measurement path diagnostics (sensors, transducers), validation and reconstruction of measurement data from sensors in the measuring paths in real time. The dedicated tools (Intelligent Real Time Diagnostic System − iRTDS) built into the system of a hierarchical, multi-threaded control optimizing system of SCADA system allow to obtain advanced diagnostics of technological processes in real time using HPC technology. In effect of the application of the proprietary iRTDS tool, we obtain a significant rise of energy efficiency of technological processes in key sectors of the economy, which in global terms, e.g., urban agglomeration, increases the economic efficiency.