Model predictive control integrators

Backx, T. O. Bosgra and W. Marguardt. Integration of Model Predictive Control and Optimization of Processes. ADCHEM Proceedings, pp. 249-259, Pisa, Italy (2000). Baker, T. E. An Integrated Approach to Planning and Scheduling. In Foundations of Computer Aided Process Operations (FOCAPO), D. W. T. Rippin J. C. Hale and J. F. Davis, eds. CACHE Corporation, Austin, TX (1993), pp. 237-252. 

Due to the complexity of bioprocesses, and the lack of direct in-process measurements of critical process variables, much work is being done on development of soft sensors and model predictive control of such systems. Soft sensors have long been used to estimate biomass concentration in fed-batch cultivations. The soft sensors can be integrated into automated control structures to control the biomass growth in the fermentation. 

Model predictive control (MPC) was developed in the 1970s and 1980s to meet control challenges of refineries. The advantages of MPC are most evident when it is used as a multivariable controller integrated with an optimizer. The greatest MPC benefits are realized in applications with dead-time dominance, interactions, constraints, and the need for optimization. As opposed to a traditional control loop, where the controller responds to a difference (error) between the set point and measurement, the predictive controller uses a vector difference between the future trajectory of the set point and the predicted trajectory of the controlled variable as its input (Figure 2.52). 

Baldea, M., Daoutidis, P., and Nagy, Z.K. (2010). Nonlinear Model Predictive Control of integrated process systems. In Proceedings Nonlinear Control Systems (NOLCOS 2010). 

The constraint-control problem is a multivariable square control problem, and various controllers can be used, such as a discrete integral controller or a model predictive controller. 

Integrating strategic, tactical and operational supply chain decision levels in a model predictive control framework... 

In this work an MILP model which achieves the integration of all three Supply Chain (SC) decision levels is developed. Then, the stochastic version of this integrated model is applied as the predictive model in a Model Predictive Control (MPC) framework in order to incorporate and tackle unforeseen events in the SC planning problem in chemical process industries. Afterwards, the validation of the proposed approach is justified and the resulting potential benefits are highlighted through a case study. The results obtained of this particular case study are analyzed and criticized towards future work. 

In the present work, we focus on the operational planning and control of integrated production/distribution systems under product demand uncertainty. For the purposes of our study and the time scales of interest, a discrete time difference model is developed. The model is applicable to networks of arbitrary structure. To treat demand uncertainty within the deterministic supply chain network model, a receding horizon, model predictive control approach is suggested. The two-level control algorithm relies on a... 

Equations 18-81 and 18-82 illustrate multivariable proportional control for a 2 X 2 process. Multivariable control strategies can also be developed that include integral, derivative, and feedforward control action. The books by Goodwin et al. (2001) and Skogestad and Postlethwaite (2005) provide additional information. In this text, we emphasize the use of model predictive control as the method of choice for designing multivariable controllers, as discussed in Chapter 20. 

Several types of controllers have been studied in the reactive distillation literature, ranging from simple proportional-integral (PI) controllers to complex nonlinear model predictive controllers. Even if we limit ourselves to PI controllers, a variety of alternative control stmctures has been studied. 

Among other predictions, the integrated model reveals that as work rate is varied, commensurate increases in the rate of mitochondrial ATP synthesis are effected by changes in concentrations of available ADP and inorganic phosphate. In other words, mitochondrial respiratory control is achieved in vivo by substrate feedback control. The predicted relationship between substrates and work rate is plotted in Figure 7.14. Model predictions are compared to data obtained from NMR spectroscopy of exercising flexor forearm muscle in healthy human subjects [106],... 

The cellular synthesis of HA is a unique and highly controlled process. HA is naturally synthesized by a class of integral membrane proteins called hyaluronan synthases, of which vertebrates have three types HASl, HAS2, and HAS3 [19,20], See-ondaiy structure predictions and homology modeling indicate an integral membrane... 

De Thomas etal. [Ill] studied the production of polyurethanes and showed that NIRS can be used successfully to monitor the course of the reaction in real time. Spectral data were obtained with a dispersive instrument, using standard transflectance probes. An MLR model was derived for the quantitative determination of isocyanate concentrations during the urethane polymerization reaction. Model predictions were used to build statistical process control charts and to detect trends along the polymerization reaction. The authors suggested that the integration of NIRS with process control routines could lead to improvements of product quality and consistency, while minimizing reaction time. However, model predictions were not used as feedback information for any sort of correction of the process trajectory. Similar studies were performed by Dallin [112] for prediction of the acid number during the production of polyesters. 

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