In MLTot the total material holdup in the liquid phase mol Section 5 we outline the control problem we are facing and the MVTot the total material holdup in the vapour phase mol steps we have taken to tackle it. N the control horizon for the pressure controller samples 2.
Methodology and novel contributions of this work Nc the control horizon for the MIMO control samples L Pout the pressure of the liquid stream out the reboiler The key contribution of this paper is a novel approach to control bar the solvent regeneration system installed in the CO2 capture pilot plant located at Imperial College London facilities.
Schematic of the CO2 capture plant. Once a dynamic process model of adequate accuracy has been obtained and validated to the extent possible, we then begin the process of developing a suitable control technique. This task is performed via a combination of engineering judgement based on desired process performance and repeated process sim- ulation activities in order to meet the global control objective of reducing operating costs.
It should be noted that the control vari- ables selected are a function of the chosen objective function. In this work, we have selected the reboiler pressure, the level of liq- uid accumulated in the reboiler and the rate of CO2 production. The rationale for this choice is detailed in Section 5. Once the control variables have been selected, it is then nec- essary to design a control architecture.
Finally, once model predictive control formulations which account for the future the control technique has been formulated and implemented, it is prediction of energy and CO2 prices. To that end, we present the methodology used in this work to integrate the development and application of advanced process control techniques with dynamic model of the reboiler. Models of 3. ICL Chemicalengineering, In this work, we focus on the The methodology developed is outlined below, and also pre- modelling and control of the reboiler unit, as, in addition to exert- sented graphically in Fig.
A key challenge of applying unit are detailed in Table 1. Here, we appreciably simplify the process Geometry of reboiler. Owing to the fact that we describe Diameter m 0. Schematic of the reboiler system. Thus, in this work, we use the unit is presented in Fig.
Model description eign object Kakalis et al. The models used are described in a previous paper Mac Dowell et al. These now temperature dependent. In the ensuing sections, we dependence: describe the development of the thermodynamic, process and con- trol models used in this work. Much of this complexity temperature parameter values using the Origin8 software results from the need to describe in detail the reactions occurring.
These data are not always available Fig. Peng et al. This conclu- the behaviour of the reboiler. However, even with as a single equilibrium stage comprising material and energy bal- equilibrium-based models of reacting systems, there remains the ances, equilibrium relations and summation equations. For the sake requirement to describe the equilibrium reactions.
For example, rial or energy balance equations. Thus the approximation of adiabatic operation 0 0. Equilibrium relations Fig. Isothermal Px projection of the vapour—liquid equilibrium of an aqueous solu- The streams leaving the reboiler are in chemical, mechanical and tion of monoethanolamine and carbon dioxide. Finally, a note on nomen- volumes of each stream leaving the reboiler: clature convention in this work.
Many of the variables in the model L L presented here are an explicit function of time — e. The component hold-up is distributed between the liquid and 4. Ancillary devices vapour phases: Solvent is circulated through the regeneration system by means of centrifugal pumps as shown in Fig. For all runs, the reboiler pressure was given to be 0. Control problem Experiment Broadly speaking, the aim of this work is to minimize the oper- Prediction ational cost associated with post-combustion CO2 capture.
The solvent regeneration step is by far the most energy intensive part of this process, and accounts for the vast majority of the operating costs Mac Dowell et al. Thus in this work we are using the reboiler as a proxy with which to describe the energy dynamics of T K the entire process. In general, one is concerned with the quantity of CO2 directed to storage, thus the explicit aim of this control problem is the minimi- sation of the energy demand associated with CO2 capture whilst concurrently maximizing the amount of CO2 produced from the reboiler.
It is therefore clear that CO2 capture represents a trade-off between the cost of emitting CO2 and the opportunity Fig. Steady state validation of the proposed model. Here, a calculated lean solvent cost associated with operating a CO2 capture plant.
Thus we pro- temperature is compared with an experimentally observed temperature T for a pose that the design of a suitable and feasible control technique for given energy input Qin. We wish to emphasize that the purpose of this work 4.
Steady-state model validation is not to identify a capture target. In reality, this will be an external- ity. Rather, we intend to show that, given a particular target e. Table 4. We note that there is a CO2 and energy.
In gen- eral, we tend to overpredict the reboiler temperature — this may be 5. Variable study a consequence of the assumption of adiabatic operation. In this Treb , there is an average relative deviation of 0. In addition, as both variables are directly interrelated 0. Control architecture We propose the use of a two-level control architecture in order Fig.
The reason for a given energy input Qin. In this work, we have Fig. Control architecture. This is amount. We note that this inequality end-point constraint maybe illustrated in Fig. Before solving the optimization problem, the to control the reboiler pressure. The results of this study are illustrated in pressure loop from the rest of variables in order to avoid inducing Fig. As shown in Fig. High-level control the reboiler.
The problem is formulated in Fig. Similarly to the MPC Camacho and Bordons, , the to explicitly take advantage of the time varying market prices of moving horizon is equivalent to performing the optimization in energy and CO2. Thus, this optimization will be solved each hour with the prediction of the market prices of the 24 future hours. Optimization performance with sliding horizon. Low-level control In this subsection, the low-level control illustrated in Fig.
As mentioned in Section 5. The reboiler pressure, Preb , is controlled by an indepen- Fig. The inputs and disturbances to each control loop see Fig. The inlet loading, Rich , as shown in Fig. The pressure is the fastest variable with an a time 5. Control model constant of approximately 4 s. The resulting trol model and control performance should be found. Pressure model. For the case of the pressure control loop, a model for the reboiler pressure and temperature is required.
The 5. Level and CO2 capture models. Conse- a model of both variables is required to design and implement the quently, the pressure control will include a temperature constraint. This kind of model add complexity to the surable disturbance as this variable depends on the process units implementation and tuning of MPC controllers causing sluggish downstream of reboiler.
The solvent level is composed of three control performance as presented in Camacho and Bordons Thus, the design of an external closed-loop observer is avoided. Pressure control. The model of the system presented in Eq. It is important to observe that for both The cost function is formulated as: cases models of order higher than 1 present zeros located out the unity circle as seen in Fig.
Level and CO2 capture control. The formulation followed for this case is again the GPC for- The cost function is rewritten as: mulation. Percentage savings for different amounts of CO2 captured. Simulation platform. In all cases, operating the solvent regeneration process in a while the controllers and economic optimisation are implemented dynamic fashion proved to be the most cost-optimal solution. The in MatlabTM. The control models calculate the values tion is presented in Fig.
Inspection of Fig. Section 5. Thus, in Eq. These values are then used as inputs to the dynamic reboiler mcp,24 is taken to be equal to kg. As described above, mcp,24 is equal to kg, but as we are operating the system in a non-steady-state 6. High-level control fashion, the value of FCO2 ,sp will vary in each time period. The setpoint of the control variable, FCO2 ,sp , calculated for the 6. High-level control calculation for 24 h prices shown in Fig.
In making this decision, we explicitly coincides with the hours of cheaper energy prices. As discussed previously, day calculated. The prices of the CO2 captured has been gathered the total amount of CO2 captured during a single 24 h period is from the Forexpros Financial Markets Wordwide website forexpros equal to the determined value, kg. The blue line corresponds to The time-varying nature of both the wholesale energy and the setpoint calculated by the economic optimization with a sam- CO2 prices is evident from this data.
The net cost of 24 h operation was change in setpoint to no more than 1. In this way we ensure considered to be the difference between the cost of CO2 emission the safe operation of this process. High-level control calculation for 1 month 70 10 We note, however, that the results shown in Fig. It is therefore of interest to examine the reliability of this approach.
Thus, in Fig. Energy and CO2 market prices for a day. Setpoint calculated by solving the economic optimization. Simulated measurable disturbances. An important challenge faced during the tuning of the PID con- Fig. Analysis of the percentage savings during a month: a energy price; b percentage savings; and c h CO2 capture. To deal with this problem, the samp- ling time was reduced to 0. One may observe that the price trend shown here the PID pressure controller.
This issue was not encountered whilst is broadly similar to that shown in Fig. Also, the MPC controller responds faster than the over night, with two distinct peaks in morning and evening time. PID controller to variations in the CO2 capture setpoint. Further, We calculate the values of CO2 captured per day, and also the rel- the responses of the MPC controller to changes in the measur- ative cost difference between steady-state and dynamic operation able disturbances are superior as the feedforward is included in as described in Section 6.
We observe that operating the solvent the formulation presented previously. In Figs. We emphasize 6. Low-level control that the measurable disturbances are held constant during these simulations as they were tested as described in the Section 6. Then, the performance of the low-level control was validated 2 0. The objective of implementing a 0. The reboiler level loses performance to avoid overshoot in the capture control.
We assume that both variables are related to the capture plant and the variations occur simultaneously. The solvent outlet Fig. That The temperature is never greater than K which is the decom- is, we regenerate the solvent to a greater extent when energy is position temperature of the solvent. This constraint is indicated by relatively cheap, and allow CO2 to accumulate in the circulating sol- a red dashed line in Fig. We note that this energy of CO2 capture processes in ensuring the cost-effective availability consumption includes the sensible heating of solvent and the of power-plants.
From Fig. The total net cost for a day is over D This saving rep- Inspection of Fig. This percentage is strongly rate. Furthermore, level of solvent in the reboiler. As can be observed from Fig. It was the biggest domain-only sale in years.
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