In process systems engineering, the design of economically profitable and operationally optimal processes has been an active area of research for over 25 years. Significant contributions have been made throughout the years that consider the problem through a variety of approaches, particularly the consideration of (i) operability and design under uncertainty [1] as well as (ii) the system controllability with regulatory control structure selection and its tuning [2, 3] and (iii) the design and model predictive control with stability and robustness aspects taken into account, in a unified framework [4]. During the last few years there has also been great interest in the field of optimal operation, with several studies in literature exploring the interactions between shorter term operational decisions (regulatory and model-based control) with their longer term counterparts (scheduling and planning techniques) thus attempting to bridge the gap between the two [5-8]. The aim of this approach targets the long-term optimal operation of a process while taking into account its control aspects and the inherent handling of uncertainty [9].
In this work we present a study where the interactions of all three aspects of decision making of the process -- from the design aspects of the process and control elements to the optimal scheduling and control strategies -- in a single framework are considered, through its application on a series on micro-CHP units destined for domestic use. We consider the design aspects of the process as uncertain during the design procedure of the model-based controllers as well as the rolling-horizon scheduling. The latter takes into account the dynamics of the system through the consideration of the interactions between the control and scheduling via the use of a bridging model [5]. The integrated system of control and scheduling is solved into a multi-parametric fashion, thus allowing the acquisition of the exact solution of the problem a priori, while considering both (i) the design aspects, and (ii) the operational aspects of the process as uncertain. The optimally operational process is then introduced to dynamic optimisation which targets the design optimisation.
The principles introduced in [10] form the basis for this work. More specifically, a high fidelity model of an internal combustion engine equipped, natural gas powered, domestic CHP plant has been developed and introduced into the gPROMS® simulation and optimisation environment [11]. The model is able to simulate the simultaneous production of electrical power and usable heat. Aspects of the model such as the size of the internal combustion engine as well as a hot water buffer tank are considered for the optimal design. The model is subjected into approximation techniques in order for simplified state-space models with minimal loss of accuracy to be acquired. The approximation technique involves the use of the System Identification Toolbox of MATLAB®. Two subsystems are identified which reflect into the inherent distinct operation of the CHP -- the power generation subsystem and the heat recovery subsystem. The approximation procedure takes into account the design variables of the high fidelity model. The design of the mp-MPCs is based on the afore-mentioned state space models thus resulting into a design dependent decentralized control scheme [12]. The principles described in [13] are followed for the design of the rolling-horizon scheduling approach. In this case, the design aspects of the process are also treated as uncertainty. Finally, the design dependent bridging model is developed that closes the gap between the short-term and long-term operation of the process. All three rolling-horizon policies are introduced into the gPROMS® environment via the development of a software tool described extensively in [10]. Figure 1 graphically represents the set-up of the system and its components.
Figure 1 CHP System setup for domestic use with design uncertainty
At this point, the system can be simulated for given horizons, for a range of designs. The system is then introduced into the gOPT® optimisation functionality of gPROMS® that targets the design optimisation. At every iteration of the single vector shooting optimisation procedure, the design of the system is explored for a predefined demand profile, thus causing the uncertainty to realize itself. The operational objective of the design-dependent control is minimal mismatch to a set-point which is provided by the economically optimal solution of the design-dependent rolling-horizon scheduling and bridging model. The dynamic design optimisation targets the cost of acquisition of the system components via the transition of the cost on a daily basis, given a 10-year loan repayment.
Through this technique, we manage to target all three aspects of designing economically and operationally optimal processes with the use of a high-fidelity model and design-dependent rolling-horizon strategies. Most importantly though, we manage to provide a framework that can be applied to a large variety of processed without compromising the accuracy that a high-fidelity model provides and at the same time using advanced control and scheduling techniques in a closed-loop fashion.
References
1. van Schijndel, J.M.G. and E.N. Pistikopoulos, Towards the integration of process design, process control and process operability - Current stares and furore trends. Foundations of Computer-Aided Process Design, 2000. 96: p. 99--112.
2. Mohideen, M.J., J.D. Perkins, and E.N. Pistikopoulos, Optimal design of dynamic systems under uncertainty. AIChE Journal, 1996. 42(8): p. 2251--2272.
3. Bansal, V., et al., Simultaneous design and control optimisation under uncertainty. Computers & Chemical Engineering, 2000. 24(2‐7): p. 261--266.
4. Sakizlis, V., J.D. Perkins, and E.N. Pistikopoulos, Parametric Controllers in Simultaneous Process and Control Design Optimization. Industrial & Engineering Chemistry Research, 2003. 42(20): p. 4545--4563.
5. Baldea, M. and I. Harjunkoski, Integrated production scheduling and process control: A systematic review. Computers & Chemical Engineering, 2014. 71: p. 377--390.
6. Zhuge, J. and M.G. Ierapetritou, Integration of scheduling and control for batch processes using multi-parametric model predictive control. AIChE Journal, 2014. 60(9): p. 3169--3183.
7. Subramanian, K., C.T. Maravelias, and J.B. Rawlings, A state-space model for chemical production scheduling. Computers & Chemical Engineering, 2012. 47(0): p. 97--110.
8. Engell, S. and I. Harjunkoski, Optimal operation: Scheduling, advanced control and their integration. Computers and Chemical Engineering, 2012. 47: p. 121-133.
9. Patil, B.P., E. Maia, and L.A. Ricardez-Sandoval, Integration of scheduling, design, and control of multiproduct chemical processes under uncertainty. AIChE Journal, 2015. In press
10. Pistikopoulos, E.N., et al., PAROC-An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science, 2015.
11. Diangelakis, N.A., C. Panos, and E.N. Pistikopoulos, Design optimization of an internal combustion engine powered CHP system for residential scale application. Computational Management Science, 2014. 11(3): p. 237--266.
12. Diangelakis, N.A. and E.N. Pistikopoulos, A Decentralised Multi-parametric Model Predictive Control Study for a Domestic Heat and Power Cogeneration System, in Computer Aided Chemical Engineering2015. Accepted for publication
13. Kopanos, G.M. and E.N. Pistikopoulos, Reactive scheduling by a multiparametric programming rolling horizon framework: A case of a network of combined heat and power units. Industrial and Engineering Chemistry Research, 2014. 53(11): p. 4366-4386.
See more of this Group/Topical: Computing and Systems Technology Division