Bioenthanol Production Simulation and Control

L. NIETO; D. ZUMOFFEN; M. BASUALDO

Hydrogen Production: Prospects and Processes

Nova Science Publishers, Inc.

Lugar: New York; Año: 2012; p. 295 - 351

In this chapter topics such as modeling, simulation and control of a bio-ethanol processor system (BPS) for hydrogen production are addressed. The main objective is obtain a hydrogen quality such that be adequate to feed a proton exchange membrane fuel cell (PEM-FC). The dynamic model of the heat integrated process is developed around the proposed operating point provided from the synthesis procedure. It is implemented by a proper connection between two well known commercial softwares such as HYSYSr and MATLABr. The main part of the plant are the reactors, modeled in the MATLABr environment. The auxiliary equipments and property package information is obtained from HYSYSr . The control structure, able to maintain the system around the selected working point, is determined by applying a new systematic approach dedicated to address this complex problem. In this chapter a new methodology, called minimum square deviation (MSD), for solving simultaneously the optimal sensor location (OSL) and the control structure selection (CSS) problems, via optimization, is detailed. This approach was tested by the authors on several academic cases/plants with different degrees of complexity and dimension. In this chapter it is extended for a challenging process, the BPS. It is a medium-scale system and represents a good example for defining the plantwide control structure during the design step. Basically, the MSD approach tries to minimize the use of heuristic concepts and is able to work ifonly steady-state information is available. The OSL problem is solved by considering the sum of square deviations (SSD or the square Frobenius matrix norm) of the uncontrolled variables from their nominal values when a full internal model control (IMC) is used. The CSS problem is solved by accounting a new interaction index called net load effect (NLE) so as to deal properly with a trade-off between set point changes and disturbances effects. A suitable parametrization of the above problems allows minimizing both the uncontrolled variables deviation and the NLE in a SSD sense. It is solved via stochastic global search implemented with genetic algorithms (GA). As a result, the conceptual engineering of this novel process is presented and tested through a complete set of numerical simulations including both open and closed loop behavior of the bio-ethanol processor system for an efficient H2 production.