Composition control and estimation with temperature measurements for multicomponent distillation columns

In this Thesis, the problem of controlling and estimating compositions of interest in a multicomponent column with temperature measurements is addressed. This work is motivated by the necessity of (i) regulating some of the effluent compositions around the prescribed values for a given separation, in spite of disturbances entering the column as fluctuations in feed flow rate, composition and temperature, and (ii) estimating such compositions in order to monitor the separation. These problems cannot be practically solved by composition analyzers, because of their high costs of purchase and maintenance, their reliability issues and the delays in obtaining composition measurements. A possible solution is the employment of temperature measurements, which do not present the abovementioned shortcomings. In a multicomponent distillation column, the compositions in a tray are not uniquely determined by the temperature at the same tray, and therefore temperature sensors cannot provide composition control and estimation without offset. Thus, a methodology that permits the achievement of composition control and estimation objectives within a predetermined tolerance is necessary. The methodology illustrated in this Thesis is applied to a six-component C3-C4 splitter through simulation examples in order to be assessed on the basis of the structural results, according to different control and estimation objectives. Both control and estimation problems are solved through a unified methodology that consists in the partition of both problems into a structural and an algorithmic part, puts together techniques already employed and new ideas, and permits a systematic design. As regards the control part, the structure is defined as the joint selection of (i) the temperatures to be regulated and (ii) their pairing with the chosen manipulated variables, while the algorithm is the dynamic data processor that processes temperature measurements in order to perform the control task. On the other hand, regarding the estimation part, the structure is defined as the joint selection of (i) a (possibly reduced) estimation model, (ii) the number and locations of temperature measurements, (iii) the states to be innovated through injection of temperature measurements and (iv) the regions in which the temperature can be approximated as the average one, while the algorithm is the dynamic data processor that processes temperature measurements in order to perform the estimation task. In this Thesis, we propose the temperature gradient with per-component contribution diagram as the mean for the selection of part of the control structure and of the entire estimation structure. Such diagram is the main tool of the used methodology, and consists in plotting temperature gradients at a nominal steady-state together with the contributions due to the components. The diagram is based on the well-known temperature slope criterion, which has extensively been used in control but, as will be shown in this Thesis, provides a simpler and more intuitive criterion for both control and estimation purposes in a multicomponent column, compared to other well-known methods. As concerns the control algorithm selection, conventional and cascade-type con-trollers with the assistance of first-order observers are developed on the basis of passivity concepts. Such controllers provide a systematic and easy tuning criteria and an anti-windup protection. Their employment is here extended to a multicomponent case. Geometric Estimator (GE) (with partial innovation) and Extended Kalman Filter (EKF) (with complete or partial innovation) based algorithms are chosen in order to carry out the estimation task. The GE approach, which provides a systematic tuning criterion, is here extended to a multicomponent case.