Optimization Model and PID Temperature Control System Design for CO2 Capture Process by CaO Carbonation-CaCO3 Calcination Cycles a
Wei Lia, Chao Daia Energy and Environmental Research center North China Electric Power University Beijing, China E-mail:
[email protected]
Abstract- CO2 capture processes by carbonation-calcination cycles of CaO/CaCO3 were limited by the carbonation conversion and sorbents reutilization with the number of carbonation/calcinations cycles. In order to optimizing the CaO/CaCO3 cycles, BP neural network model and PID temperature control system were established based on the simulation of the process parameters and dynamic characteristics. The carbonization/calcination temperature, the mass fraction of additives for sorbents and calcination time were selected for the input conditions, while the output conditions were capture capacity and the reutilization of sorbents. Genetic algorithm(GA) model is established to optimize the PID controller's proportional coefficient kP, integral coefficient kI, and differential coefficient kD. The results indicated that BPNN coupled with PID model could form a complete optimization strategy for CO2 capture process by CaO/CaCO3 cycles. Keywords-CO2 capture; CaO/CaCO3 cycles; BPNN; GA; PID controller;
I.
INTRODUCTION
As the major contributor of greenhouse gases (GHG), CO2 is not only the main combustion product of coal-thermo-power plant, but also the product of flue gas desulfurization process. Mitigation, immobility and reutilization of CO2 been widely investigated. It is generally accepted [1] that the cost associated with the separation of CO2 from flue gas introduces the largest economic penalty. This paper focus on the separation of CO2 from flue gas stream using regenerable sorbents based on the carbonation/calcination loop of CaO/CaCO3, which can avoid the low temperatures operation and reduce energy consumption. However, it also consumes energy ranging from 25% to 37%, and the main power plant costs increased by about 60% due to the large size parts and the high costs of equipment. The evolution of the capture capacity of Ca-based sorbents in CaO/CaCO3 loops, over a number of cycles (Fig.1), has been
Lei Liu*a,b, Yalou Li c
b
Department of Civil Engineering, Dalhousie University Halifax, Canada c China Electric Power Research Institute Beijing, China
Figure 1. Diagram of the proposed calcination/carbonation loop[1]
studied in previous works varying the process variables [2]. The establishment of kinetics models can simulate the reaction process between sorbents and CO2, but most of the models out of consideration on the optimization of parameters [3]. BP Neural Network(BPNN) is an artificial neural network with error back propagation, characteristics of strong self-learning, adaptive, anti-interfere, etc[4]. In this paper, BPNN was designed to simulating and optimizing CaO/CaCO3 loops decarbonization process based on its inherent ability to approximate any nonlinear continuous function. The critical design parameters carbonization/calcination temperature, the mass fraction of additives of sorbents and calcination time were selected as the input conditions, the capture capacity and the reutilization of sorbents were the output conditions. II.
MODEL ESTABLISHMENT
A. The optimization process on critical process parameters The optimization process can be considered as a basic type of model based predictive system in which the model is a trained neural network (Fig. 2).Firstly, the optimization aim of the multi-objective optimization problem were the highest value of removal rate of CO2(T1) and calcium-based sorbents recycle rate(T2). Secondly, according to BPNN model, the constraint conditions of the objective function are determined as the value range of four factors, as followed (1),(2) [5,6]: Max T1=net(Z1, Z2, Z3, Z4) Max T2=net(Z1, Z2, Z3, Z4) S.t.
(1) (2)
700≤Z1≤950, 10≤Z2≤30, 400≤Z3≤600 and 0.3≤Z4≤0.6 Thirdly, in order to seek the optimal solution for BP neural network model, 50 group samples were used to train the BP net, the training process was described as Fig,2(a), the solving steps was listed as Fig.2(b). 1) Design a desired aim of the process and the reaction initial condition which must met the constraint conditions above. 2) Predict the process results by using the BP model whose input is reaction initial condition. 3) Count A, and A equal predicted value minus expectations value
*Author for correspondence. This research was supported by the Ministry of Education of the P.R.C (No.708017). Support for Li Wei was also provided by the Young Teachers Scholarship, North China Electric Power University, China.
978-1-4244-4813-5/10/$25.00 ©2010 IEEE
established, and checked by SIMULINK program, to optimize the gained parameters kP, kI, and kD of the controller on line. The experiment on the Matlab was carried out to verify the new state-space model which was used to design the controllers. A. PID algorithm In the simulation system, the PID algorithm formula is: P(t ) = K p [e(t ) +
1 TI
∫ e(t )dt +
TD de(t ) ] dt
(1)
Making the last formula discrete, then digital differential equations is described as: P ( k ) = K P {E ( k ) +
T TI
k
∑ E ( j) + T
D
E ( k ) − E (k − 1) } T
(2) （where T is sampling period） According to recursive principle, available incremental formula is: j =0
P(k) = P(k −1) + Kp[E(k) − E(k −1)]+ KI E(k) + KD[E(k) − 2E(k −1) + E(k − 2)] Δ P ( k ) = P ( k ) − P ( k − 1)
= K p [ E (k ) − E (k − 1)] + K I E (k ) + K D [ E (k ) − 2 E (k − 1) + E (k − 2)]
(3) Therefore, incremental PID algorithm program is designed as: Δ PP ( k ) = K
p
Δ PI ( k ) = K
[ E ( k ) − E ( k − 1 )] I
E (k )
Δ PD ( k ) = K D [ E ( k ) − 2 E ( k − 1) + E ( k − 2 )]
(4)
Δ P (k ) = Δ PP (k ) + Δ PI (k ) + Δ PD (k )
(5) According to the system input and output conditions, selecting the appropriate PID parameters, the system output can track the given input fast, stable and accurately, achieve effective control of the CaO/CaCO3 cycles temperature by the combination GA model with conventional PID controller.[8] Figure 2. The optimization program on the CaO/CaCO3 cycles process
4) Judgement, if A
