Chemical Processing - October 2007 - (Page 52)

>> mAking it work Distillation control Manipulated Variable Feed flow rate 1st-effect steam flow rate Side-stripper stream flow rate Reflux flow rate Controlled Variable 190-proof concentration Differential temperature Differential pressure 190-proof product flow rate Table 1. The application predicts changes in controlled variables to moves in manipulated variables and corrects as necessary. mum water removal at lower energy consumption. Achieving effective control Dealing with azeotropic ethanol/water separation and the pressure-swing operations of the molecular sieves required a complex model. Pavilion’s application relies on a nonlinear Model Predictive Control (MPC) methodology — a control algorithm, based on a dynamic model of the process, predicts and optimizes the process’s future response. The MPC algorithm finds the process’s optimum operating point by computing a sequence of manipulated variables’ moves in each control interval. MPC can handle the constraint challenges and disturbances of a highly nonlinear, multivariable system such as the distillation/sieves arrangement in the ethanol process. The BSE application relies on a combination of first principles and empirical models identified from input/output measurements. These models where created with Pavilion’s state-of-the-art empirical modeling technology combined in a hybrid fashion with well-known fundamental models of ethanol/water separation and sieve adsorption. Empirical modeling alone is often used in MPC because solving a set of complex differential equations in first principles >> Distillation and dehydration Vacuum pump Azeotrope 190 proof ethanol Liquid A Fermented mash Rectiﬁer column 1 Vapor B Stillage Beer column Side stripper 200 proof ethanol Liquid Molecular sieves Recycle to front end Vapor Liquid 200 proof ethanol Recylce to front end 2 3 S Vapor Second effect steam Figure 1. Three columns followed by a bank of three molecular sieves produce high purity ethanol. 52 • October 2007 models for the calculation of the optimal sequences at each control interval can be unfeasible. However, first principles models can add great value to the application by providing understanding of process behavior outside of previous operating ranges (e.g., at 5% higher capacity). Pavilion’s application for the ethanol industry combines comprehensive empirical models and available first-principles models with a constraint-handling multivariable control algorithm. The first principles models were developed based on heat- and masstransfer systems and validated using operating data from plant tests also used to develop the empirical models. The MPC application obtains the current process information and, based on its dynamic model, calculates a sequence of future process moves over a specified time period. An optimization algorithm determines the correct responses of the manipulated variables so that several objectives can be achieved simultaneously. The control system dynamically compares the predicted trajectory with the process response and corrects for any differences detected. Because of this dynamic compensation and the taking into account of process constraints, the control system is stable and operationally reliable. Using small steps, the stable process is led gradually to its optimal operating point while larger steps can bring prompt corrections if disturbances affect the system. The application for the distillation system controls the production rate and quality of the 190-proof product that goes to the sieves. It also keeps the pressure and temperature constraints within the limits to avoid flooding and reduces ethanol losses. Table 1 lists some of the control components for the distillation process. In addition to controlling the differential temperature and pressure www.chemicalprocessing.com http://www.chemicalprocessing.com

Table of Contents Feed for the Digital Edition of Chemical Processing - October 2007

Chemical Processing - October 2007 Contents From the Editor ChemicalProcessing.com Field Notes In Process Energy Saver Compliance Advisor Biofeedstocks See Real Growth Become a Cyber-Security Pacesetter Go Beyond Condition Monitoring Disposable Equipment Earns Lasting Role Improve Control Loop Performance Ethanol Plant Boosts Output and Saves Energy Process Puzzler Plant InSites Equipment & Services Ad Lits Product Spotlight/Classifieds Ad Index End Point

Chemical Processing - October 2007

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