Statistical Evaluation of Quantities Measured in the Detection of Soil Air Pollution of the Environmental Burden

Comparison of Different Approaches to the Creation of a Mathematical Model of Melt Temperature in an LD Converter

In the steel production process in the LD converter, it is important to have information about the melt temperature. The temperature and chemical composition of the steel are important parameters in this process in terms of its completion. During the process, continuous measurement of the melt temperature and thus also information about the end of the process are missing. This paper describes three approaches to creating a mathematical model of melt temperature. The first approach is a regression model, which predicts an immeasurable melt temperature based on other directly measured process variables. The second approach to creating a mathematical model is based on the machine learning method. Simple and efficient learning algorithms characterize the machine learning methods. We used support vector regression (SVR) method and the adaptive neuro-fuzzy inference system (ANFIS) to create a mathematical model of the melt temperature. The third approach is the deterministic approach, which is based on the decomposition of the process and its heat balance. The mathematical models that were compiled based on the mentioned approaches were verified and compared to real process data.

Design of a Low-Cost Electrostatic Precipitator to Reduce Particulate Matter Emissions from Small Heat Sources

Capturing particulate matter (PM) is an important issue due to the protection of human health and the quality of their life. This paper describes the innovation of an affordable particulate matter capture device for small heat sources to reduce particulate matter emissions. The design of two investigated variants of the device is based on the principle of a tubular electrostatic precipitator with one charging electrode placed in the chimney. The design of the precipitators is aimed at increasing the area of the collecting electrodes by elements dividing precipitation space, with a simultaneously increased number of charging electrodes. The influence of the elements’ application on the pressure drop and the gas flow velocity through the devices is analyzed by computational fluid dynamics (CFD). The work is further focused on the economic evaluation of precipitators and design adjustments for lower energy consumption. The achieved results show the right direction of efforts to improve the equipment designed to capture PM emissions.

The Prediction of Possibilities of CO Poisoning and Explosion during Syngas Leakage in the UCG Process

Underground coal gasification (UCG) is an industrial process that converts coal into product gas (syngas). This technology makes it possible to obtain energy without mining coal and post-processing. The UCG process is considered a safe technology with various advantages over conventional mining techniques. However, a small amount of potentially dangerous syngas may escape from the UCG cavity, and it is necessary to pay attention to the safety of the process in this respect. This article analyses the impact of a syngas leak from UCG on a possible CO poisoning and explosion hazard in the vulnerable zones. Measured data from experiments and proposed mathematical models were used for the analysis of the UCG process and simulation studies. It is mainly a mathematical model mixing of gases, which evaluated the danger of explosion and CO poisoning in sensitive zones. This model predicts the composition of the syngas mixture with air because this mixture can be dangerous at a certain concentration. Simulation studies focused mainly on CO poisoning in vulnerable areas based on the measured data from laboratory experiments. Simulation studies have shown that the critical value of CO poisoning can achieve with a certain composition of syngas and its escape into vulnerable areas. The results of the studies here indicate a possible hazard. However, by monitoring and control of UCG process, this risk can be minimized. Based on the obtained results, the control of the supply of fresh air to the endangered area during the monitoring of the syngas composition was proposed to prevent the occurrence of possible poisoning.

Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion

The underground coal gasification represents a technology capable of obtaining synthetic coal gas from hard-reached coal deposits and coal beds with tectonic faults. This technology is also less expensive than conventional coal mining. The cavity is formed in the coal seam by converting coal to synthetic gas during the underground coal gasification process. The cavity growth rate and the gasification queue’s moving velocity are affected by controllable variables, i.e., the operation pressure, the gasification agent, and the laboratory coal seam geometry. These variables can be continuously measured by standard measuring devices and techniques as opposed to the underground temperature. This paper researches the possibility of the regression models utilization for temperature data prediction for this reason. Several regression models were proposed that were differed in their structures, i.e., the number and type of selected controllable variables as independent variables. The goal was to find such a regression model structure, where the underground temperature is predicted with the greatest possible accuracy. The regression model structures’ proposal was realized on data obtained from two laboratory measurements realized in the ex situ reactor. The obtained temperature data can be used for visualization of the cavity growth in the gasified coal seam.