Gasification technology is often seen as a synonym for the clean and efficient processing of solid fuels into combustible gas containing mainly carbon monoxide and hydrogen, the two basic components of synthesis gas. First and foremost, the facts that gas may be cleaned and that a mixture with any composition may be prepared in a relatively easy and inexpensive manner influence the possibility of using gas produced in the energy and chemical industries. In the energy industry, gas may be used directly to generate heat and electricity in the systems of a steam power plant or in combined cycle systems. It is also possible to effectively separate CO2 from the system. However, in chemistry, synthesis gas may be used to produce hydrogen, methanol, synthetic gasolines, and other chemical products. The raw material for gasification is full-quality pulverized coal, but a possibility of processing low-quality sludges, combustible fractions separated from municipal waste as well as industrial waste also exists. Despite such a wide application of technology and undoubted advantages thereof, making investment decisions is still subject to high uncertainty. The paper presents the main technological applications of gasification and analyzes the economic effectiveness thereof. In this context, significant challanges for the industrial implementation of this technology are discussed
A modified approach to equilibrium modelling of coal gasification is presented, based on global thermodynamic analysis of both homogeneous and heterogeneous reactions occurring during a gasification process conducted in a circulating fluid bed reactor. The model is based on large-scale experiments (ca. 200 kg/h) with air used as a gasification agent and introduces empirical modifications governing the quasi-equilibrium state of two reactions: water-gas shift and Boudouard reaction. The model predicts the formation of the eight key gaseous species: CO, CO2, H2O, H2, H2S, N2, COS and CH4, volatile hydrocarbons represented by propane and benzene, tar represented by naphthalene, and char containing the five elements C, H, O, N, S and inorganic matter.
Hydrogen as a raw material finds its main use and application on the Polish market in the chemical industry. Its potential applications for the production of energy in fuel cell systems or as a fuel for automobiles are widely analyzed and commented upon ever more frequently. At present, hydrogen is produced worldwide mainly from natural gas, using the SMR technology or via the electrolysis of water. Countries with high levels of coal resources are exceptional in that respect, as there the production of hydrogen is increasingly based on gasification processes. China is such an example. There some 68% of hydrogen is generated from coal. The paper discusses the economic efficiency of hydrogen production technologies employing lignite gasification, comparing it with steam reforming of natural gas technology (SMR). In present Polish conditions, this technology seems to be the most probable alternative for natural gas substitution. For the purpose of evaluating the economic efficiency, a model has been developed, in which a sensitivity analysis has been carried out. An example of the technological process of energy-chemical processing of lignite has been presented, based on the gasification process rooted in disperse systems, characteristics of the fuel has been discussed, as well as carbon dioxide emission issues. Subsequently, the assumed methodology of economic assessment has been described in detail, together with its key assumptions. Successively, based on the method of discounted cash flows, the unit of hydrogen generation has been determined, which was followed by a detailed sensitivity analysis, taking the main risk factors connected with lignite/coal and natural gas price relations, as well as the price of carbon credits (allowances for emission of CO2) into account.
The research was aimed at examining the impact of the petrographic composition of coal from the Janina mine on the gasification process and petrographic composition of the resulting char. The coal was subjected to fluidized bed gasification at a temperature below 1000°C in oxygen and CO2 atmosphere. The rank of coal is borderline subbituminous to bituminous coal. The petrographic composition is as follows: macerals from the vitrinite (61.0% vol.); liptinite (4.8% vol.) and inertinite groups (29.0% vol.). The petrofactor in coal from the Janina deposit is 6.9. The high content of macerals of the inertinite group, which can be considered inert during the gasification, naturally affects the process. The content of non-reactive macerals is around 27% vol. The petrographic analysis of char was carried out based on the classification of International Committee for Coal and Organic Petrology. Both inertoid (34.7% vol.) and crassinetwork (25.1% vol.) have a dominant share in chars resulting from the above-mentioned process. In addition, the examined char contained 3.1% vol. of mineroids and 4.3% vol. of fusinoids and solids. The calculated aromaticity factor increases from 0.75 in coal to 0.98 in char. The carbon conversion is 30.3%. Approximately 40% vol. of the low porosity components in the residues after the gasification process indicate a low degree of carbon conversion. The ash content in coal amounted to 13.8% and increased to 24.10% in char. Based on the petrographic composition of the starting coal and the degree of conversion of macerals in the char, it can be stated that the coal from the Janina deposit is moderately suitable for the gasification process.
The aim of the paper is the petrographic characterization of coal from the Wieczorek mine and the residues after its gasification. The coal was subjected to gasification in a fluidized bed reactor at a temperature of about 900°C and in an atmosphere of oxygen and CO2. The petrographic, proximate, and ultimate analysis of coal and char was performed. The petrographic composition of bituminous coal is dominated by macerals of the vitrinite group (55% by volume); macerals of inertinite and liptinite groups account for 23% and 16.0%, respectively. In the examined char, the dominant component is inertoid (41% vol.). Mixed dense and mixed porous account for 10.9% and 13.5% vol., respectively. In addition, the examined char also contained unreacted particles such as fusinoids, solids (11.3% vol.), and mineroids (5.1% vol.). The char contains around 65% vol. of low porosity components, which indicates a low degree of carbon conversion and is associated with a low gasification temperature. The char was burned and the resulting bottom and fly ashes were subjected to petrographic analysis. Their composition was compared with the composition of ashes from the combustion of bituminous coal from the Wieczorek mine. Bottom ashes resulting from the combustion of bituminous coal and char did not differ significantly in the petrographic composition. The dominant component was mineroid, which accounted for over 80% vol. When it comes to fly ash, a larger amount of particles with high porosity is observed in fly ash from bituminous coal combustion.
In this study, non-sintered ceramsite was prepared using coal gasiﬁcation coarse slag obtained from a methanol plant. The basic performance and heavy metal leaching toxicity were analyzed. The results showed that seven out of nine non-sintered ceramsite groups were in accordance with the national standard of compressive strength (5 MPa), while only three groups met the national standard of water absorption index of less than 22%. The heavy metal concentrations in these three groups were found to be lower than that speciﬁed in National Class IV of surface water environment standards. The concentration of Cr was found to be 16.45 μg/L, which represents only 1% of the IV standard. The optimum mixing ratio, which showed high compressive strength (6.76 MPa) and low water absorption (20.12%), was found to be 73% coal gasiﬁcation coarse slag, 15% cement, and 12% quartz sand. The characterization using Fourier transform infrared spectroscopy showed that the formation of gelatin in ceramsite enhances the performance of the ceramsite base and increases the immobilization of heavy metal. The study proved that the preparation of non-sintered ceramsite using coal gasiﬁcation coarse slag reduces its environmental risk and achieves efﬁcient utilization of the slag. Therefore, it can be concluded that it is a feasible and environmental friendly method for the disposal of coal slag.
Based on data collected during an UCG pilot-scale experiment that took place during 2014 at Wieczorek mine, an active mine located in Upper Silesia (Poland), this research focuses on developing a dynamic fire risk prevention strategy addressing underground coal gasification processes (UCG) within active mines, preventing economic and physical losses derived from fires. To achieve this goal, the forecasting performance of two different kinds of artificial neural network models (generalized regression and multi-layer feedforward) are studied, in order to forecast the syngas temperature at the georeactor outlet with one hour of anticipation, thus giving enough time to UCG operators to adjust the amount and characteristics of the gasifying agents if necessary. The same model could be used to avoid undesired drops in the syngas temperature, as low temperature increases precipitation of contaminants reducing the inner diameter of the return pipeline. As a consequence the whole process of UGC might be stopped. Moreover, it could allow maintaining a high temperature that will lead to an increased efficiency, as UCG is a very exothermic process. Results of this research were compared with the ones obtained by means of Multivariate Adaptative Regression Splines (MARS), a non-parametric regression technique able to model non-linearities that cannot be adequately modelled using other regression methods. Syngas temperature forecast with one hour of anticipation at the georeactor outlet was achieved successfully, and conclusions clearly state that generalized regression neural networks (GRNN) achieve better forecasts than multi-layer feedforward networks (MLFN) and MARS models.