Modeling and optimization of a semiregenerative catalytic naphtha reformer has been cam'ed out considering most of its key constituent units. A detailed kinetic scheme involving 35 pseudocomponents connected by a network of 36 reactions in the C, -C,, range was modeled using Hougen-Watson Langmuir-Hinshelwood-type reaction-rate expressions. Deactivation of the catalyst was modeled by including the corresponding equations for coking kinetics. The overall kinetic model was parameterized by benchmarking against industrial plant data using a feed-characterization procedure developed to infer the composition of the chemical species in the feed and reformate from their measured ASTM distillation data. For the initial optimization studies, a constant reactor inlet temperature configuration that would lead to optimum operation over the entire catalyst life cycle was identified. The analysis was extended to study the time-optimal control profiles of decision variables over the run length.In addition, the constant octane case was also studied. The improvement in the objective function achieved in each case was determined. Finally, the sensitivity of the optimal results to uncertainty in reactor-model parameters was evaluated. IntroductionCatalytic naphtha reforming is practiced extensively in the petroleum-refining industry to convert gasoline boiling-range low-octane hydrocarbons to high-octane gasoline compounds for use as high-performance gasoline fuel. This is accomplished by conversion of n-paraffins and naphthenes in naphtha to isoparaffins and aromatics over bifunctional catalysts such as Pt/AI,O, or Pt-Re/Al,O,. Recent environmental legislation in the United States has banned the use of lead as an additive for boosting antiknock properties of motor fuel. Coupled with these stricter environmental regulations, there has been a consistent increase in the demand for higher fuel efficiency standards of engines. This requires the use of higher compression ratios in engines, and therefore motor fuel with an even greater octane number. These considerations have continually forced the refiner toward producing higher-octane-number products from their catalytic naphtha reformers. This can be achieved by reforming the naphtha under more severe conditions, but this will also cause an increase in the rate of coke deposition, resulting in the reduction of cycle lengths of the catalyst. Due to these trade-offs and others, there is ample potential for optimization of a catalytic naphtha reformer. A proper selection of operating conditions within plant constraints is essential to maximize the profitability of the reformer. Due to the catalyst deactivation with time, the process assumes a transient nature and its optimization results in a time-optimal problem. This makes the optimization problem more challenging to solve. However, in the absence of an analysis of this kind, the unit may remain under suboptimal operating conditions, resulting in significant economic losses.Use of mathematical models as a tool for either off-line or on...

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In-line neutralization of wastewater pH is demonstrated by using a multicomponent process simulator, which includes noise, instrument lags, and six nonstationary parameters. By contrast, the controller action is based on a model that considers the wastewater as a single fictitious acid of unknown concentration and unknown Gibbs free energy of dissociation. Measurable information from a dual base injection is used for a least-squares parameterization of the two-coefficient model. Control over a wide range of wastewater compositions and upsets indicates rapid and effective in-line control without the blending volume required in most pH control strategies.

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The problem of on-line optimization of a model IVjluidized catalytic cracking (FCC) unit is analyzed. The "process"was modeled by combining a model IV FCC unit dynamic simulator (McFarlane et al., 1993;Khandalekar, 1993) with a ten-lump yield model (Jacob et al., 1976;Arbel et al., 1995) (NPSOL, Gill et al., 1986) and the optimum process set points were implemented on the process simulator using a nonlinear constraint controller (Kandalekar and Rigs, 1995). The relatiue per$ormance of constraint control, off-line optimization, and on-line optimization is compared for different feed characteristics and product pricing structures.