Real-time optimization

A fourth, optimization, tier can be naturally integrated into the control structure delineated above. In particular, the models of the overall process behavior in the intermediate (5.21) and slow (5.28) time scales are non-stiff, and can be used to formulate well-conditioned optimization problems for computing the setpoints of the corresponding controllers, respectively, y p and.  

1 The use of a non-square controller (e.g., an MPC), such that the number of manipulated inputs is lower than the number of controlled variables, is certainly possible. While this approach eschews the use of cascaded configurations, it is intuitively detrimental to closed-loop performance due to the reduced number of manipulated variables. 

Dynamics and control of generalized integrated process systems 

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A real-time optimization (RTO) system determines set point changes and implements them via the computer control system without intervention from unit operators. The RTO system completes all data transfer, optimization c culations, and set point implementation before unit conditions change and invahdate the computed optimum. In addition, the RTO system should perform all tasks without upsetting plant operations. Several steps are necessaiy for implementation of RTO, including determination of the plant steady state, data gathering and vahdation, updating of model parameters (if necessaiy) to match current operations, calculation of the new (optimized) set points, and the implementation of these set points. 

All of these methods have been utilized to solve nonlinear programming problems in the field of chemical engineering design and operations (Lasdon and Waren, Oper. Res., 5, 34, 1980). Nonlinear programming is receiving increased usage in the area of real-time optimization. 

Some recent applications have benefited from advances in computing and computational techniques. Steady-state simulation is being used off-line for process analysis, design, and retrofit process simulators can model flow sheets with up to about a million equations by employing nested procedures. Other applications have resulted in great economic benefits these include on-line real-time optimization models for data reconciliation and parameter estimation followed by optimal adjustment of operating conditions. Models of up to 500,000 variables have been used on a refinery-wide basis. 

There is a need for methods that can determine global optima for arbitrary nonlinear functions, and that can handle extremely large nonlinear models for real-time optimization (on the order of millions of variables). 

The performance of a biotreatment system ultimately depends on optimization of the activity of microbes and the ability to control the process parameters of the treatment system [157]. In this respect, the ability to monitor gene copy numbers and gene expression is highly useful for real time optimization of the efficiency of a biotreatment system. Advanced molecular techniques as well as low cost methods (e.g., antibody detection of enzymes based on color reaction strips fluorescence i.e., GFP marked organisms with UV light detection) can also be applied to monitor the microbial community structure, persistence of the added bacteria, and their interactions with indigenous populations. 

Other synonyms for steady state are time-invariant, static, or stationary. These terms refer to a process in which the values of the dependent variables remain constant with respect to time. Unsteady state processes are also called nonsteady state, transient, or dynamic and represent the situation when the process-dependent variables change with time. A typical example of an unsteady state process is the operation of a batch distillation column, which would exhibit a time-varying product composition. A transient model reduces to a steady state model when d/dt = 0. Most optimization problems treated in this book are based on steady state models. Optimization problems involving dynamic models usually pertain to optimal control or real-time optimization problems (see Chapter 16)... 

Schmid, C. and L. T. Biegler. Reduced Hessian Successive Quadratic Programming for Real Time Optimization. Proceed IFAC Adv Control Chem Processes, Kyoto, Japan, 173-178 (1994). 

Geddes, D. and T. Kubera. Integration of Planning and Real-Time Optimization Olefins Production. Comput Chem Eng 24 1645-1649 (2000). 

Lowery, R. P B. McConville F. H. Yocum and S. R. Hendon. Closed-Loop Real Time Optimization of Two Bisphenol-A Plants. Paper presented at the National AIChE Meeting, Houston, TX, Mar. 28-Apr. 1, 1993. 

Plantwide real-time optimization Set point changes... 

In plant operations planning each refinery model produces target operating conditions, stream allocations, and blends across the whole refinery, which determines (a) optimal operating conditions, flows, blend recipes, and inventories and (b) costs, cost limits, and marginal values to the scheduling and real-time optimization (RTO) models. 

Diagram showing the combination of real-time optimization and model predictive control in a computer control system. 

R. Lestage, A. Pomerleau, D. Hodouin, Constrained real-time optimization of a grinding circuit using steady-state linear programming supervisory control, Powder Technol. 124 (2002) 254-263. 

Dynamic simulation, process control, real-time optimization Process synthesis, flowsheet convergence, simultaneous modular vs. equation-oriented... 

Fatora III, F. C. and Ayala, J. S. Successful Closed Loop Real-Time Optimization Hydrocarbon Processing (June 1992). 

Fatora III, F. C., Gochenour, G. B., Houk, B. G., and Kelly, D. N., "Closed-Loop Real-Time Optimization and Control of a World Scale Olefins Plant" Paper Presented at the National AIChE Meeting, New Orleans, Louisiana (April 1992). 

Kelly, D. N. Fatora HI, F. C. and Davenport, S. L., Implementation of a Closed Loop Real-Time Optimization on a Large Scale Ethylene Plant" Paper Presented at the Meeting of the Instrument Society of America" Anaheim, California (October 1991). 

Plant-wide and individual unit real-time optimization, parameter estimation, supervisory control, data reconciliation... 

Workstations. Workstations are the most powerful computers in the system, capable of performing functions not normally available in other units. A workstation acts both as an arbitrator unit to route internodal communications and as the database server. An operator interface is supported, and various peripheral devices are coordinated through the workstations. Computationally intensive tasks, such as real-time optimization or model predictive control, are implemented in a workstation. Operators supervise and control processes from these workstations. Operator stations may be connected directly to printers for alarm logging, printing reports, or process graphics. 

Another situation when the use of the statistical model can be a good choice over the RSM is when the deterministic model is excessively complex. For example, when the process is described by a distributed parameters model, the steady-state mass and energy balances are differential equations. The use of differential equations as constraints in an optimization problem makes its solution difficult and increases the incidence of convergence problems. In this case, solving the optimization problem using the statistical model is much simpler. The statistical model can also be used when the computational effort to solve the optimization problem using the deterministic model is too high, as can be the case for real-time optimization problems. 

It should be noted that the optimization problems solved for levels 2 and 3 begin to merge as the plantwide optimization begins to set targets for the unit operations in many process units. This large-scale, frequent optimization of operating conditions is known as real-time optimization (RTO). RTOs are run approximately every 30 minutes to 1 hour, with the resulting optimal setpoints downloaded to model predictive controllers (MPC). 

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