scholarly journals Model Predictive Control of UCG: An Experiment and Simulation Study

2019 ◽  
Vol 48 (4) ◽  
pp. 557-578
Author(s):  
Jan Kacur ◽  
Patrik Flegner ◽  
Milan Durdan ◽  
Marek Laciak
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Coal Gasification ◽  
Technical Problem ◽  
Calorific Value ◽  
Multivariate Adaptive Regression Splines ◽  
Underground Coal Gasification ◽  
Mining Equipment ◽  
Real Process

Underground coal gasification (UCG) is a potential technology that enables to mine coal without traditional mining equipment. The coal is gasified deep in underground and produced syngas is processed on the surface. The most important technical problem in UCG is unstable quality of syngas and control. This paper proposes advanced control based on an adaptive predictive controller. The maintaining of desired calorific value depends on flow rates of gasification agents injected to the underground geo-reactor and controlled exhaust. The paper proposes a physical model of UCG technology and applies a method of multivariate adaptive regression splines (MARS) to model the gasification process. This method satisfactorily approximates nonlinearity in the process variables. The paper proposes adaptive model predictive control (MPC) using online model estimation and applied it on the MARS model of UCG that imitates the real process. The results have shown that optimization of manipulation variables can replace manual control in UCG. Getting better quality of syngas depends on setpoints, optimized manipulation variables, and constraints used in MPC. In simulations, the adaptive MPC has shown better performance in comparison with manual and PI control.

scholarly journals A COMPARATIVE STUDY OF DATA-DRIVEN MODELING METHODS FOR SOFT-SENSING IN UNDERGROUND COAL GASIFICATION

2019 ◽  
Vol 59 (4) ◽  
pp. 322-351
Author(s):  
Ján Kačur ◽  
Milan Durdán ◽  
Marek Laciak ◽  
Patrik Flegner
Keyword(s):  
Coal Gasification ◽  
Calorific Value ◽  
Multivariate Adaptive Regression Splines ◽  
Ex Situ ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Underground Coal Gasification ◽  
Process Data ◽  
Adaptive Regression ◽  
Data Driven Modeling

Underground coal gasification (UCG) is a technological process, which converts solid coal into a gas in the underground, using injected gasification agents. In the UCG process, a lot of process variables can be measurable with common measuring devices, but there are variables that cannot be measured so easily, e.g., the temperature deep underground. It is also necessary to know the future impact of different control variables on the syngas calorific value in order to support a predictive control. This paper examines the possibility of utilizing Neural Networks, Multivariate Adaptive Regression Splines and Support Vector Regression in order to estimate the UCG process data, i.e., syngas calorific value and underground temperature. It was found that, during the training with the UCG data, the SVR and Gaussian kernel achieved the best results, but, during the prediction, the best result was obtained by the piecewise-cubic type of the MARS model. The analysis was performed on data obtained during an experimental UCG with an ex-situ reactor.

scholarly journals A model predictive controller of the blowing mode during basic oxygen furnance process

2021 ◽  
pp. 61-66
Author(s):  
Oleksandr Stepanets ◽  
Yurii Mariiash
Keyword(s):  
Mathematical Model ◽  
Cast Iron ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Basic Oxygen Furnace ◽  
Scrap Metal ◽  
Basic Oxygen ◽  
The World ◽  
The Cost

Today in Ukraine and the world, the problem of energy saving and reducing the cost of smelted steel is state of art. Metallurgical enterprises are developing in conditions of fierce competition, the main reason is that Ukrainian products are extremely energy-intensive due to the depreciation of fixed assets and outdated technological processes. The basic oxygen furnace process is a process of producing steel from liquid cast iron with the addition of steel scrap to the converter and blowing oxygen from above through a water-cooling lance. Nowadays, the production of steel by BOF process is the most popular in the world and is becoming increasingly common. The main disadvantage of the basic oxygen furnace is the need to provide the initial amount of heat (in the form of liquid cast iron) and as a consequence - restrictions on the processing of scrap metal. Reducing the cost of basic oxygen furnace steel is achieved by increasing the share of scrap metal by increasing the degree of afterburning of CO to CO2 in the cavity of the converter, by optimal control of the parameters of the blast mode using model-predictive control. The principle of model-predictive control is based on a mathematical model of the plant. This approach minimizes the functional that characterizes the quality of the process. The linear-quadratic functional was chosen. A forecasting model is proposed taking into account the constraint on changing the position of the lance and the pneumatic oxygen supply valve. It was found that the change in the rate of decarburization of the metal depends on the distance of the lance to the level of the quiet bath and affects the degree of afterburning of CO to CO2. The decarburization process is non-stationary, described by a first-order inertial model, the transfer coefficient and time constant of which depends on the melting period and the duration of the purge. The mathematical model of the blast mode of oxygen-converter melting has been improved, taking into account the influence of the blast intensity on the decarburization process of the bath, which allowed to increase the accuracy and quality of blast control in terms of changing oxygen flow during purging. The simulation results of the automatic control system show that the model-predictive regulator provides the required level of carbon dioxide in the converter gases when the flow rate of oxygen for purge changes.

scholarly journals Effect of Lignite Properties on Its Suitability for the Implementation of Underground Coal Gasification (UCG) in Selected Deposits

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5816
Author(s):  
Krzysztof Kapusta
Keyword(s):  
Moisture Content ◽  
Coal Gasification ◽  
Calorific Value ◽  
Process Efficiency ◽  
Average Moisture Content ◽  
Underground Coal Gasification ◽  
Gasification Process ◽  
Laboratory Setup ◽  
Large Bulk

Two experimental simulations of underground coal gasification (UCG) processes, using large bulk samples of lignites, were conducted in a surface laboratory setup. Two different lignite samples were used for the oxygen-blown experiments, i.e., “Velenje” meta-lignite (Slovenia) and “Oltenia” ortho-lignite (Romania). The average moisture content of the samples was 31.6wt.% and 45.6wt.% for the Velenje and Oltenia samples, respectively. The main aim of the study was to assess the suitability of the tested lignites for the underground coal gasification process. The gas composition and its production rates, as well as the temperatures in the artificial seams, were continuously monitored during the experiments. The average calorific value of gas produced during the Velenje lignite experiment (6.4 MJ/Nm3) was much higher compared to the result obtained for the experiment with Oltenia lignite (4.8 MJ/Nm3). The Velenje lignite test was also characterized by significantly higher energy efficiency, i.e., 44.6%, compared to the gasification of Oltenia lignite (33.4%). The gasification experiments carried out showed that the physicochemical properties of the lignite used considerably affect the in situ gasification process. Research also indicates that UCG can be considered as a viable option for the extraction of lignite deposits; however, lignites with a lower moisture content and higher energy density are preferred, due to their much higher process efficiency.

Ex Situ UCG Model Experiments with Oxygen Enriched Air in an Artificial Coal Seam

2017 ◽  
Vol 737 ◽  
pp. 379-384
Author(s):  
Fa Qiang Su ◽  
Ken-ichi Itakura ◽  
Akihiro Hamanaka ◽  
Gota Deguchi ◽  
Kohki Sato ◽  
...  
Keyword(s):  
Coal Seam ◽  
Coal Gasification ◽  
Calorific Value ◽  
Close Correlation ◽  
Gas Production ◽  
Ex Situ ◽  
Underground Coal Gasification ◽  
Coal Consumption ◽  
Gas Leakage ◽  
Gasification Reaction

Underground Coal Gasification (UCG) demands precise evaluation of the combustion area in the coal seam. Especially, the monitoring of fracture activity in the coal seam and around rock is important not only for efficient gas production but also for estimation of subsidence and gas leakage to the surface. For this objective, laboratory experiments were conducted using the simulated UCG models. This paper also investigated gas energy for coal consumption, the production gas quantity and heat value, the application of oxygen element balance in the gasification reaction process, and the gas composition obtained in this study. During burning of the coal, temperatures inside the coal, contents of product gases and acoustic emission (AE) activities were monitored successively under the control of feeding gas (air/oxygen and steam) flow rate. Comparison of the temperature variation and accumulated AE event curves revealed a close correlation between them. The local change of temperature inside the coal induced fractures with AE. The AE activity was related closely to the local changes of temperature inside the model. The evaluation of gas energy recovery calculated from the obtained product gas provided a fair evaluation for the coal consumed, and the quantity of gas product and calorific value obtained from the UCG process.

scholarly journals Experimental Study of Reverse Underground Coal Gasification

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2949 ◽  
Author(s):  
Hongtao Liu ◽  
Feng Chen ◽  
Yuanyuan Wang ◽  
Gang Liu ◽  
Hong Yao ◽  
...  
Keyword(s):  
Growth Rate ◽  
Flame Front ◽  
Oxygen Concentration ◽  
Coal Gasification ◽  
Calorific Value ◽  
Injection Rate ◽  
Underground Coal Gasification ◽  
Gasification Process ◽  
Combustible Gases ◽  
Different Characteristics

Underground coal gasification (UCG) produces less pollution and is safer than traditional coal mining. In order to investigate the effects of different gasifying agents or comprehensive analyses of the characteristics of the gas components in the three zones for the reverse underground coal gasification process, a model test was carried out. The results showed that the oxygen concentration of a gasifying agent is recommended to be higher than 21%, which will lead to more combustible gases and a higher calorific value of gas. Higher flow rates and oxygen content generally afforded more desirable gas compositions and calorific values, with the latter as high as 1430.19 kcal/Nm3. For the enriched oxygen gasifying agent in the reverse gasification process, the flow increase from 10 to 20 Nm3/h affords a rapid increase in the growth rate of the flame front, from 1.80 to 4.88 m/day, which is much faster than that for the air gasifying agent. Increasing the gas injection rate and oxygen concentration will increase the growth rate of the flame front. This affects the distribution of the three zones and further leads to different characteristics of the gas components.

scholarly journals Microgrids Power Quality Enhancement Using Model Predictive Control

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 328
Author(s):  
Felix Garcia-Torres ◽  
Sergio Vazquez ◽  
Isabel M. Moreno-Garcia ◽  
Aurora Gil-de-Castro ◽  
Pedro Roncero-Sanchez ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Power Systems ◽  
Electric Power ◽  
Power Quality ◽  
Predictive Control ◽  
Electric Power System ◽  
Standard Requirement ◽  
Electrical Grid ◽  
Sinusoidal Waveform

In electric power systems, any deviation with respect to the theoretical sinusoidal waveform is considered to be a disturbance in the power quality of the electrical grid. The deviation can alter any of the parameters of the waveform: frequency, amplitude, and symmetry among phases. Microgrid, as a part of the electric power system, has to contribute providing an adequate current waveform in grid connected-mode, as well as to guarantee similar voltage features than the standard requirement given for public distribution grids under normal exploitation conditions in islanded mode. Adequate power quality supply is necessary for the correct compatibility between all the devices connected to the same grid. In this paper, the power quality of microgrids is managed using a Model Predictive Control (MPC) methodology which regulates the power converters of the microgrids in order to achieve the requirements. The control algorithm is developed for the following microgrids working modes: grid-connected, islanded, and interconnected. The simulation results demonstrate that the proposed methodology improves the transient response in comparison with classical methods in all the working modes, minimizing the harmonic content in the current and the voltage even with the presence of non-balanced and non-harmonic-free three-phase voltage and current systems.

Evaluating the costs and achievable benefits of extending technologies for uneconomical coal resources in South Africa: the case of underground coal gasification

2008 ◽  
Vol 19 (4) ◽  
pp. 21-31 ◽  
Author(s):  
M. Zieleniewski ◽  
A.C. Brent
Keyword(s):  
South African ◽  
Coal Gasification ◽  
Economic Value ◽  
Calorific Value ◽  
Underground Coal Gasification ◽  
Direct Process ◽  
Coal Resources ◽  
Geological Surveys ◽  
Safety And Health ◽  
Expected Benefits

As the South African economy relies heavily on its coal resources, these resources should be utilised and managed in the best possible manner. Underground coal gasification (UCG) is one of the leading technologies used where conventional min-ing techniques are uneconomical. UCG delivers gas suitable for synthesis, production of fuels and elec-tricity, or for home usage. The method is perceived as being environmentally friendly and safer than traditional mining. The study summarised in this paper was conducted so as to create a simple model that would allow for the evaluation of UCG process-related costs versus expected benefits in a wider context and under different circumstances. The parameters of the model are: feasibility definition, i.e. maximum possible gas calorific value, based on geological surveys and gasification agents for a pre-defined need; direct process-related costs that are derived from the expected capital and operational expenditures and compared to the value and vol-ume of the gas produced; and assessment of exter-nality costs, i.e. the indirect economic value of envi-ronmental, safety and health benefits. The external-ities concept should encourage governmental agen-cies to consider further investment in UCG technol-ogy as a vehicle for delivering, potentially, high sav-ings in terms of the reduction in the costs of envi-ronmental damage resulting from gaseous emis-sions into the atmosphere, specifically expenditure on national health.

An Optimization Model of High-Speed and High-Precision Machining Based on Model Predictive Control

2018 ◽  
Author(s):  
Jing Zhang ◽  
Jiexiong Ding ◽  
Qingzhao Li ◽  
Qicheng Ding ◽  
Zhong Jiang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
High Precision ◽  
Predictive Control ◽  
Feed Rate ◽  
Optimization Model ◽  
High Speed ◽  
Machining Process ◽  
Precision Machining ◽  
Contouring Error

In the multi-axis high-speed and high-precision machining process, the contouring error and the feed rate of tool tip and affect the quality of machined workpiece and the processing efficiency, respectively. The faster feed motion will result in greater tracking error of each axis. The contouring error which directly affects the quality of machined part is caused by the tracking errors of the axes. Obviously, it is difficult to improve the contouring accuracy and increase the feed rate simultaneously. To this end, a novel optimization model is developed here based on the model predictive control method. First, the feed servo model of translational and rotary axes are established, and the contouring error model is afterwards constructed. Subsequently, the optimization algorithm is derived to achieve the high processing speed, and input constraints are addressed to avoid violation of the performance limitation of the drivers. In addition, contouring error constraint, which is obtained by calculating the contouring error of the processed path, is addressed to high contour accuracy. Finally, a simulation is conducted to verify the effectiveness and superiority of the proposed method.

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