Miloš Banjac

Improving the energy efficiency of a 110 MW thermal power plant by low-cost modification of the cooling system:

The electric power system of the Republic of Serbia relies mostly on lignite-fired thermal power plants, with 70% of the total electricity generation. Most of these plants are over 30 years old, and investment in their modernization is necessary. The energy efficiency of the 110 MW coal-fired power plant in which the condenser is cooled by the mechanical draught wet cooling towers system is analyzed in this paper. Attention is primarily devoted to operating conditions of the cold end of the plant, i.e. to the interrelationship of the condenser and cooling towers. Most important parameters that affect the operation of the cooling towers system are ambient air temperature and relative humidity, specific mass flow rate, and temperature of cooled water. With the existing cooling system, the overall energy efficiency of the plant is low, especially in the summer months, even less than 30%, due to adverse weather conditions. By upgrading existing cooling tower system by adaptation of two additional cooling tower cells, overall energy efficiency can be increased by 1.5%. The cooling tower system rehabilitation investments payback period is estimated to be less than one year. Static method for economic and financial assessment is used.

IMPROVEMENT OF CFD MODELS OF TUNNEL FIRE DEVELOPMENT BASED ON EXPERIMENTAL DATA

This paper, dealing with the problems of mathematical description of the tunnel fire development process with the use of experimental data, outlines the procedure of correction of the existing and obtaining of an improved CFD model package. The improved CFD model was developed on the basis of detailed analysis and comparison of experimental and numerical results, through consideration of the physical structure of all processes affecting combustion. During the analysis it was noticed that the existing CFD model in the part covering combustion based on the so-called Steady Laminar Flamelet model, treats the combustion process almost as a direct correlation between the processes of mixing gasses and heat release rate. This potential deficiency has been overcome by correction of the model in the section defining boundary condition for the burning surface and by establishing a direct correlation between the measured value of the fuel mass change rate and the amount of heat released from burning surface. In this way a modification of complex stoichiometric combustion processes was avoided, while providing the model that better describes and predicts the course of events in this type of complex, anisotropic and turbulent flow of gases in the tunnel.

NUMERICAL COMPUTATION AND PREDICTION OF ELECTRICITY CONSUMPTION IN TOBACCO INDUSTRY

Electricity is a key energy source in each country and an important condition for economic development. It is necessary to use modern methods and tools to predict energy consumption for different types of systems and weather conditions. In every industrial plant, electricity consumption presents one of the greatest operating costs. Monitoring and forecasting of this parameter provide the opportunity to rationalize the use of electricity and thus significantly reduce the costs. The paper proposes the prediction of energy consumption by a new time-series model. This involves time series models using a set of previously collected data to predict the future load. The most commonly used linear time series models are the AR (Autoregressive Model), MA (Moving Average) and ARMA (Autoregressive Moving Average Model). The AR model is used in this paper. Using the AR (Autoregressive Model) model, the Monte Carlo simulation method is utilized for predicting and analyzing the energy consumption change in the considered tobacco industrial plant. One of the main parts of the AR model is a seasonal pattern that takes into account the climatic conditions for a given geographical area. This part of the model was delineated by the Fourier transform and was used with the aim of avoiding the model complexity. As an example, the numerical results were performed for tobacco production in one industrial plant. A probabilistic range of input values is used to determine the future probabilistic level of energy consumption.