Dynamic optimization of fixed bed chemical-looping combustion processes

Han, Lu; Bollas, George M.

doi:10.1016/j.energy.2016.07.031

Cited by 42 publications

(20 citation statements)

References 65 publications

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“…Cooling water is controlled by electric valve and regulated by upper computer. The agitator is installed in the tank, and the external motor drives the agitator shaft and the impeller to rotate, providing corresponding power for the material circulation [Han and Bollas (2016)]. The heating system of the reactor is 12 thermal resistance heating rods installed in the jacket, which are evenly distributed around the reactor and controlled by adjusting the conduction angle of crystal valve tube by PLC.…”

Section: Methodology 21 the Systematic Structure Of Batch Reactormentioning

confidence: 99%

A Control Algorithm for the Optimization of Batch Reactor-Based Processes

Bai¹,

Liu²

2019

Fluid Dynamics &Amp; Materials Processing

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Levenberg-Marquardt (LM) algorithm is applied for the optimization of the heat transfer of a batch reactor. The validity of the approach is verified through comparison with experimental results. It is found that the mathematical model can properly describe the heat transfer relationships characterizing the considered system, with the error being kept within ±2°C. Indeed, the difference between the actual measured values and the model calculated value curve is within ±1.5°C, which is in agreement with the model assumptions and demonstrates the reliability and effectiveness of the algorithm applied to the batch reactor heat transfer model. Therefore, the present work provides a theoretical reference for the conversion of practical problems in the field of chemical production into mathematical models.

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Section: Methodology 21 the Systematic Structure Of Batch Reactormentioning

confidence: 99%

A Control Algorithm for the Optimization of Batch Reactor-Based Processes

Bai¹,

Liu²

2019

Fluid Dynamics &Amp; Materials Processing

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“…As before, steady-state optimization was first performed for the plant operating at full load. The set points ofṁ IP1 ,ṁ IP2 ,ṁ LP1 andṁ LP2 were manipulated by the supervisory control layer to maximize the plant's efficiency, as shown by Equation (2). The bounds of admissible inputs are shown in Table 3, with the optimal values presented in Table 7.…”

Section: Case Study Ii: Optimization Variablesṁ Ip1 ṁ Ip2 ṁ Lp1 Andmentioning

confidence: 99%

“…The excessive emissions of CO 2 from fossil-fueled power plants contribute to the greenhouse effect and global warming. Increasing the efficiency of power generation cycles and integration with CO 2 capture units are nowadays accepted as the most promising short-term approaches to reducing CO 2 emissions while we transition to renewable and carbon free energy sources [1,2]. Efficiency improvements can be achieved through the optimization of power plant operating strategies or through modification of plant design.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization of a Subcritical Steam Power Plant Under Time-Varying Power Load

Chen¹,

Bollas²

2018

Processes

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The increasing variability in power plant load in response to a wildly uncertain electricity market and the need to to mitigate CO2 emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort and are the subjects of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model was used as a test-bed for dynamic simulations, in which the coal load was regulated to satisfy a varying power demand. Plant-level control regulated the plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant’s operation at varying loads was optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems were formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points was shown to be feasible with corresponding savings in coal consumption of 184.8 tons/day and a carbon footprint decrease of 0.035 kg/kWh.

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“…Regarding this issue, the solid combustion in CLC was simulated as a result of developing the mathematical modeling. Also the modelling were performed on the fuel reactor for different gases with different oxygen carriers such as Parker (2014), Han and Bollas (2016), Porrazzo et al (2016), Menon and Patnaikuni (2017), Zhang et al (2017), Haus et al (2018) and Hamidouche et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Numerical and experimental analysis for simulating fuel reactor in chemical looping combustor system

Ismail

Ding

Ramzy

et al. 2020

Int J Coal Sci Technol

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The greenhouse problem has a significant effect on our communities such as, health and climate. Carbon dioxide is one of the main gases that cause global warming. Therefore, CO2 capture techniques have been the focus of attention these days. The chemical looping combustion technique adopted the air reactor and fuel reactor to recycle heat energy. This study presents a numerical and experimental investigation on a fuel reactor in chemical looping combustor (CLC) system. The present numerical model is introduced by the kinetic theory of granular flow and coupled with gas–solid flow with chemical reactions to simulate the combustion of solids in the CLC. The k–ε turbulent model was used to model the gas phase and the particle phase. The developed model simplify the prediction of flow patterns, particle velocities, gas velocities, and composition profiles of gas products and the distribution of heterogeneous reaction rates under the same operating conditions. The predicted and experimental results were compared according to the basis of determination coefficient (R2). In addition the results showed that there is a good agreement between the predicted and experimental data. The value of (R2) for CO, CO2 and CH4 was 0.959, 0.925 and 0.969 respectively. This shows that the present model is a promising simulation for solid particle combustion and gives the power direction for the design and optimization of the CLC systems.

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Dynamic optimization of fixed bed chemical-looping combustion processes

Cited by 42 publications

References 65 publications

A Control Algorithm for the Optimization of Batch Reactor-Based Processes

A Control Algorithm for the Optimization of Batch Reactor-Based Processes

Dynamic Optimization of a Subcritical Steam Power Plant Under Time-Varying Power Load

Numerical and experimental analysis for simulating fuel reactor in chemical looping combustor system

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