Actual Plant Data Research Articles

Dynamic model for a dense medium drum separator in coal beneficiation

Dense medium drum (DMD) separators are unit processes that are typically used to beneficiate coal, iron ore and other minerals by making use of density separation. Some coal dense medium separation plants typically include a DMD separator. The operational management of this unit process is often limited to localised control of medium density and feed mass flow rate. Dynamic models for coal dense medium separation have been developed by the authors with the intention of using them for dynamic control.A suitable dynamic model for a DMD separator could not been found in the available literature. This paper shows how the dynamic model for a dense medium cyclone has been applied to a DMD separator. The model parameters were determined and the performance of the model is evaluated using actual plant data from a Wemco drum. Coal washability and drum partitioning behaviour are used to estimate the grade of the product for model grade simulation and validation.

Improved steady-state models for chlorine dioxide delignification sequences that include washer carryover effects

In previous studies, generalized steady-state models were proposed to approximate the chlorine dioxide demand needed for the delignification of softwood and hardwood pulps, where the kappa number entering the bleach plant can fluctuate. However, these expressions neglect the effect of dissolved solids with the stock that originate from incomplete pulp washing. In this study, the original elemental chlorine-free (ECF) models are modified to include the effects of carryover from brownstock or post-oxygen washing. The stoichiometric bleach consumption from carryover, based on its composition, was calculated from various literature sources. The majority of the bleach demand (about 70%) results from the dissolved lignin contained in the brownstock carryover, with the remainder resulting from the inorganic sulfur constituents (e.g., sulfide and thiosulfate). When the effect of brownstock carryover was taken into account, the modified models accurately predicted the amount of chlorine dioxide consumed for a given delignification level (about ±0.1% chlorine dioxide) vs. actual bleach plant data. The improved models can be used to gauge the level of washer carryover entering the bleach plant if this parameter is not regularly monitored by the mill. Additionally, these modified expressions could be integrated into advanced process control strategies for ECF bleaching where the washer carryover or dissolved lignin entrainment is measured with online sensors.

Unscented Kalman filter for a coal run-of-mine bin

This paper describes the application of an unscented Kalman filter to a coal run-of-mine bin. A dynamic model of the bin is derived using the principle of mass conservation. The dynamic model is nonlinear with unknown parameters that are identified using actual plant production data. The identified dynamic model is used by an unscented Kalman filter to update the states of the system to improve model output accuracy. The derived bin model with and without an unscented Kalman filter is compared with actual plant data. Results show that the unscented Kalman filter can significantly improve the plant outputs estimated by the bin model on its own.

Data mining – new perspectives on predicting coke quality in recovery stamp charged coke making process

Data mining technique has been used to develop the coke quality prediction model for stamp charge coke making. The developed model is being used to optimize the coke strength after reaction based on its coal blend properties and operating parameters. The study shows the actual plant coke CSR and predicted coke CSR of same coal blend were much closer proximity. Actual plant data have been used for developing and validation of the model. The technique was successfully used to produce superior coke from the same coke oven batteries without damaging the oven health.

Exergetic assessment of a rotary kiln for clinker production in cement industry

Cement industry is one of the most energy consuming sectors in the world. Through a cement plant, rotary kiln is the most energy intensive section that consumes the highest portion of the total energy, so it is getting very substantial to perform energy and exergy analyses for determining possible energy–saving options. In this study, thermodynamic assessment of the rotary kiln unit of a cement plant is carried. For the analysis, mass, energy and exergy balances of the rotary kiln are constituted and using actual plant data the performances of the rotary kiln are identified. The rotary kiln clinker capacity is 79 tons per hour. The energy and exergy efficiency of the unit is found to be 68% and 58.6%, respectively. At the end of the study, the results are compared with the other studies concerning with the same matter of different plants and available heat recovery possibilities are discussed.

Multiple regression analysis of performance parameters of a binary cycle geothermal power plant

Regression analysis of a 7.35MWe existing binary geothermal power plant is conducted using actual plant data to assess the plant performance. The thermo physical properties of geothermal fluid and ambient conditions, which are brine (geothermal water) temperature and flow rate, steam and NCGs (non-condensable gases) flow rates and ambient air temperature, directly affect power generation from a geothermal power plant. Generally, amount of power generated is calculated by deterministic formulations of thermodynamics. However, the data would be probabilistic because inputs may be measured by uncalibrated devices or some parameters may be neglected during the calculation. In these cases, the performance of power plant may be estimated by using regression analysis and then changing of plant performance may be monitored over time. All measured parameters on DORA-1 Geothermal Power Plant from 2006 to 2012 and 49,411 hourly time series data are used in this study.A review of the available literature indicates this paper is the first study to focus on the prediction of power generation of a geothermal power plant by using multiple linear regression analysis. In this study, annual multiple linear regression models are developed to estimate the performance of a geothermal power plant. These models are tested by using classical assumptions of linear regressions and positive serial autocorrelation is found in all models. Autocorrelations are eliminated by using Orcutt–Cochran method.Although the performance model trends, from 2006 to 2008, are found to be close, the performance status of the plant is generally variable from year to year. According to annual regression models, since 2009, the plant performance started to decline with 270kWe electricity generation capacity. The total degradation of the plant performance reached 760kWe capacity by 2012.

Modified two-phase model with hybrid control for gas phase propylene copolymerization in fluidized bed reactors

In order to explore the dynamic behavior and process control of reactor temperature, a modified two-phase dynamic model for gas phase propylene copolymerization in a fluidized bed reactor is developed in which the entrainment of solid particles is considered. The modified model was compared with well-mixed and two-phase models in order to investigate the dynamic modeling response. The modified two-phase model shows close dynamic response to the well-mixed and two-phase models at the start of the polymerization, but begins to diverge with time. The proposed modified two-phase and two-phase models were validated with actual plant data. It was shown that the predicted steady state temperature by the modified two-phase model was closer to actual plant data compared to those obtained by the two-phase model. Advanced control system using a hybrid controller (a simple designed Takagi–Sugeno fuzzy logic controller (FLC)) integrated with the adaptive neuro-fuzzy inference system (ANFIS) controller was implemented to control the reactor temperature and compared with the FLC and conventional PID controller. The results show that the hybrid controller (ANFIS and FLC controller) performed better in terms of set point tracking and disturbance rejection compared to the FLC and conventional PID controllers.

Modelling and Simulation of MSF Desalination Plant: The Effect of Venting System Design for Non-Condensable Gases

The presence of non-condensable gases )NCGs( such as carbon dioxide, nitrogen, oxygen, and argon caused by air leakages to stages and the release of dissolved gases in brine have, even small proportions, great effect on the heat transfer rate at the vapour side of the stage condensers. Due to the low conductivity of these gases, they work as insulation material and decrease the heat transfer rate and in turn they affect the plant performance. In this work, a dynamic mathematical model is developed and implemented to analyse the effect of NCGs in MSF desalination process using gPROMS software. The model is based on coupling the mass balance, energy balance and heat transfer equations with supporting correlations for physical properties calculations. The effect of NCGs on heat transfer rate and the variation of the amount of NCGs from stage to stage are studied. The present model is validated against actual plant data collected from literature. The results showed a good agreement with actual data. The results showed that the concentration of NCGs in the vapour space depends on the location of venting points and the rate release of the NCGs. Also the results revealed that the overall heat transfer coefficient (OHTC) is affected by high concentration of NCGs. The optimum location of the venting system can be obtained by variation of the venting stages.

Neural Network Modeling For Main Steam Temperature System

Main Steam Temperature (MST) is non-linear, large inertia, long dead time and load dependant parameters. The paper present MST modeling method using actual plant data by utilizing MATLAB's Neural Network toolbox. The result of the simulation showed the MST model based on actual plant data is possible but the raw data need to be pre-processed for better output. Generator output, total main steam flow, main steam pressure and total spray flow are four main parameters affected the behavior of MST in coal fired power plant boiler.

Accident modelling and safety measure design of a hydrogen station

An accident modelling approach is used to assess the safety of a hydrogen station as part of a ground transportation network. The method incorporates prevention barriers associated to human factors, management and organizational failures in a risk assessment framework. Failure probabilities of these barriers and end-states events are predicted using Fault Tree Analysis and Event Tree Analysis respectively. Results from the case study considered revealed the capability of the proposed method in estimating the likelihood of various outcomes as well as predicting their future probabilities. In addition, the scheme offers an opportunity to provide dynamic adjustment by updating the failure probability with actual plant data. Results from the analysis can be used to plan maintenance and management of change as required by the plant condition.

Experimental and Modeling Analysis of Propylene Polymerization in a Pilot-Scale Fluidized Bed Reactor

A pilot-scale fluidized bed reactor was built and used to study and gain a better understanding of the propylene polymerization process at conditions similar to those of industrial fluidized-bed reactors. Propylene polymerization reaction was carried out continuously in the reactor and dynamic temperatures, and propylene and hydrogen concentrations data were collected. Simulated dynamic profiles of the two-phase model and the well-mixed model were compared with the actual plant data. It was found that predictions of the two-phase model were in better agreement with the pilot plant data at typical industrial operating conditions. This agreement is due to the realistic assumptions of the two-phase model as compared to the well-mixed model. The maximum deviations between the pilot plant data and the two-phase model prediction for the propylene, hydrogen concentrations, reactor temperature, and polypropylene production rate were about 3.4 mol %, 0.15 mol %, 2.4 °C, and 0.8 g/s, respectively.

Analysis of Operation Parameters Affecting Hot Metal Temperature in COREX Process

Based on actual plant data, a statistical prediction model of COREX hot metal temperature has been developed to provide directions for its prediction and control by using correlation analysis. The hot metal temperature is influenced significantly by Si and S contents in hot metal, metallization, slag basicity, fuel rate, and ore consumption. A theoretical calculation model has been developed based on mass and heat balances to study the relationship between parameters and hot metal temperature quantitatively. The results show that for every 10 K increase in hot metal temperature, the tuyere oxygen volume, metallization, fuel rate, coke rate increase by 714.3 m3 h−1, 6.4%, 90.9 kg tHM−1, 29.4 kg tHM−1, respectively, while the coal rate decreases by 115.7 kg tHM−1.

Inclusion Development in Steel During Ladle Metallurgical Treatment ‐ A Process Simulation Model – Part: Industrial Validation

Steel cleanliness strongly affects the properties of the products. There is an ever increasing requirement of strict control of inclusion composition and amount during ladle treatment. A comprehensive model for inclusion development in gas stirred ladles developed by the authors is validated in the industrial ladle treatment processes. It can be used for the process simulation and optimization. The important factors, like stirring intensity, reaction between steel and slag as well as refractory material, and the conditions needed for separation and floatation of non‐metallic inclusions are taken into account. FactSage and SimuSage packages are employed as the thermodynamics database and simulation tools, respectively. The evolution of steel and slag composition along with the amount and composition of inclusions during the ladle treatment are calculated and compared with results from the industrial process. The comparison between model predictions and actual plant data shows good agreement as well for 210 t heats as for 30 t heats and different steel grades. The present model is a useful tool for simulation and optimization of ladle metallurgical treatments in industry.

Influence of Operation Parameters on Dome Temperature of COREX Melter Gasifier

Statistical analysis and theoretical calculation have been carried out to study the parameters affecting dome temperature of COREX melter gasifier by using actual plant data. The relationship between parameters and dome temperature has been realized qualitatively and quantitatively. It is found that the dome temperature is influenced significantly by metallization, fuel rate, coal rate, coke rate, and oxygen volume. High volatile matter and low moisture coal are beneficial to maintaining dome temperature. Regression analysis was carried out and equations have been developed to predict and regulate dome temperature.

Research Electricity Production Costs and External Compensation Mechanism

China's power enterprises are mainly dominated by thermal power, thermal power is the key industry of coal consumption, so the thermal power enterprises have a fundamental influence on the environment, and how to not affect the vital interests of power enterprises under the condition of power plant to increase the strength of governance is to solve the current problem of letter. On the basis of various types of power generation enterprise environmental cost of thermal power, hydropower, wind power and nuclear power pollutant treatment cost were described, through the analysis of emission control scheme and the cost compensation of pollutants, environmental governance cost model, using a actual plant data, calculation of various pollutants compensation cost of thermal power plant, and according to the thermal power and wind power for quantitative comparison before and after compensation.

Simulation of 1MWe Solar Thermal Power Plant

A grid-connected solar thermal power plant, with a gross capacity of 1 MWe at direct normal irradiance (DNI) of 600W/m2, has been designed and is being commissioned at Gurgaon near New Delhi in India. The unique feature of the plant is the integration of two different solar fields (parabolic trough collectors and linear Fresnel reflectors) without a fossil fuel backup. The hot oil (Therminol VP1) from parabolic trough collectors and saturated steam from linear Fresnel reflectors are integrated to produce superheated steam at 350°C, 42bar to run a turbine-generator to produce electricity. A simulation package has also been developed as a part of the project. This paper outlines the salient features of this package and presents simulation studies of the power plant under the climatic conditions of New Delhi. A detailed performance model of the actual plant is created in the simulator using its libraries. Diurnal simulation of the plant has been done to see the daily variations of the collector heat gain and plant power output. The plant will produce about 1365 MWh of annual energy at a capacity factor of 15.6%; the annual DNI at New Delhi being 1273 kWh/m2-year. These results can be used to plan the operation and device the appropriate control strategy of the power plant. The simulation results will be validated with actual plant data, after commissioning.

Boiler model and simulation for control design and validation

Abstract A plant validated boiler simulator is a very useful tool for controls design, analysis and performing experiments because of the cost involved in doing experiments on a real boiler or even lab-boiler. This paper presents the dynamic model simulator of a typical power-plant boiler derived based on the first principles and a hybrid approach of suitably combining lumped and distributed modeling approach for the relevant subsystems. The developed model simulator is validated using an actual plant-data covering the dynamic plant startup scenario. The plant-data of steam flow-rate, feed-water flow-rate, feed water temperature, and heat input defines the boundary condition of the plant startup, and the model-simulated response of the drum-level and pressure are compared with the actual plant-data of boiler drum-level and pressure. The validation results are very encouraging considering the challenges posed by complex behavior and uncertainties in the actual power-plant boiler-drum, and the measurement inaccuracies in the industrial plant conditions.

Model Predictive Control of a Paste Thickener in Coal Handling and Preparation Plants

The control of paste thickener is important because underflow solids concentration has to be maintained within a certain operating window to maximize water recovery while avoiding operational difficulties, e.g. pump bogging. Paste thickener dynamics is complicated due to the interactions between key process variables, uncertainties in operating conditions and large time constant. In this paper, a dynamic model of the paste thickener which addresses varying coal parameters were developed and a model predictive control (MPC) was formulated based on the model. The simulated MPC results based on actual coal processing plant data show that a significant improvement can be achieved in terms of the ability to control the underflow solids concentration as well as conforming to the constraints imposed by the physical limitations of the process, e.g. pump speed and solids amount inside the thickener.

Energy and exergy analysis of an organic Rankine for power generation from waste heat recovery in steel industry

Energy, in conjunction with exergy, analysis of a waste heat driven Organic Rankine Cycle (ORC) is performed. Using actual plant data, performance of the cycle and pinpoint sites of primary exergy destruction are assessed. Furthermore, variations of energy and exergy efficiencies of the system with evaporator/condenser pressures, superheating and subcooling are illustrated. It is observed from the analysis that, the energy and exergy efficiencies of the system are 10.2%; 48.5% and 8.8%; 42.2%, respectively, for two different actual cases. Exergy destruction of subcomponents is also quantified. The components with greater exergy destructions to lower one can be listed as evaporator, turbine, condenser and pump. Evaporation pressure has significant effect on both energy and exergy efficiencies. Pinch-point analysis is, also performed to determine effects of heat exchange process, in the evaporator, on the net power production.

A Steady State Thermal and Material Balance Model for an Iron Making Blast Furnace and Its Validation with Operational Data

In view of scarcity and depletion in the quality of raw materials as well as stringent environmental regulations, judicious use of resource and adoption of optimal operating practices have become the prime concern in iron and steel industries. Real time forecasting and supervision of the process behavior is an important step in addressing these issues. As a part of the tool termed “Real Time Process Simulator (RTPS)”, containing several reduced order models for real time monitoring and prediction of the internal dynamics of iron making blast furnace, a mathematical model for material and thermal analysis has been developed, mainly to predict the top gas composition, raw material consumption and overall heat balance. The model predictions have been tested against actual plant data. The raceway adiabatic flame temperature has been calculated using data similar to that of an operating plant. The calculated heat distribution of the process has been presented in the form of a SHANKEY diagram. The RTPS, containing the present model has been implemented in an Indian integrated steel plant. Prior to implementation, the model has been tested and validated in the plant operational range with the help of a virtual platform.