he application of a multivariable predictive controller to an activated sludge process is discussed in this work. Emphasis is given to the model identification and the long term assessment of the controller efficiency in terms of economical and environmental performances. A recurrent neural network model is developed for the identification problem and the dynamic matrix control is chosen as suitable predictive control algorithm for controlling the nitrogen compounds in the bioreactor. Using the Benchmark Simulation Model No. 1 as virtual platform, different predictive controller configurations are tested and further improvements are achieved by controlling the suspended solids at the end of the bioreactor. Based on the simulation results, this work shows the potentiality of the dynamic matrix control that together with a careful identification of the process, is able to decrease the energy consumption costs and, at the same time, reduce the ammonia peaks and nitrate concentration in the effluent
The estimation of the product composition profiles for a distillation column is addressed by using an observer based on the differential geometry theory. The estimation problem is challenging because of the markedly nonlinear behavior of the column and because of the ill-conditioning of the observability matrix at the operating condition investigated in the paper. The proposed solution is to consider the estimator structure as a degree of freedom in order to improve the estimator robustness without affecting the capability to accurately reconstruct the composition profile. Guidelines are provided to select the observer structure and the location of the measurement sensors. The observer is designed and tested experimentally on a 10-m high pilotplant distillation column separating an ethanol/water system. The results indicate that the observer is able to accurately reconstruct the composition dynamics over a wide set of operating conditions.
A stochastic formulation for the description of cooling-antisolvent mediated crystal growth processes based on the Fok- ker-Planck equation is discussed. Previous results are further extended to include not only the additional degree of free- dom (temperature) in the approach, but also to formulate the model parameters dependencies with the input manipulated variables (antisolvent flow rate and temperature) toward a global model to be used within all possible operating regimes. The obtained global models are used to define, for the first time, an operating map of the crystalliza- tion process, where asymptotic isomean and isovariance curves are reported in an antisolvent flow-rate-temperature plane. Input multiplicities are identified and validated both numerically and experimentally for the NaCl-water-ethanol nonisothermal antisolvent crystallization system
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