Weight-average reactor temperatures

Figure 9. Aspen Hydocracker case study results hydrogen consumption and weighted average reactor temperature (WART) versus product sulfur content.

For the case study, the purity of the make-up hydrogen was varied from the current value (89%) to 100%. The feed rate, recycle oil flow rate, and weighted average reactor temperature (WART) for both reactors was held constant. Under these conditions, the following effects were noted ... 

Approximate weighted average reactor temperature at start of run fApproximate hydrogen partial pressure at the high-pressure separator Approximate hydrogen-to-orl ratio at the first reactor inlet... 

Figure 20. Example Calculation of Weight Average Reactor Temperature (WABT)...

Reaction Temperature. By changing the reaction temperature, it is possible to change the octane number of the gasoline. The parameter used to characterize the reaction temperature in a reforming unit with several reactors is the weight average inlet temperature (WAIT), defined as the sum of the inlet temperature to each reactor multiplied by the weight percent of the total catalyst mass... 

After we enter the feedstock information, we must define operating temperatures and associated process variables. We enter the Reactor Control section and define the operating temperature of each bed. There are two ways specify reactor inlet temperature. In the first method, we enter the weight-average inlet temperature (WAIT) for all the reactors and specify a bias for each reactor. In the second method, we enter a reactor reference temperature and specify a bias for each reactor. We use the second method to accurately fix the inlet temperature of each bed. We recommend this method when running the model for the first time. This ensures that inlet temperatures are accurate for the purposes of calibration. We show how to input the reactors temperatures in Figure 5.58. 

The reactor used by Kizer and simulated in this work is illustrated in Figure 1. It consists of a fluidized bed preheater section feeding directly the fluidized bed reactor section. Each section was a 0.4 m high cylinder of 0.184 m diameter. The preheater contained sand and was heated by an external electrical element. The FB203S catalyst is a powder of 0.173 mm diameter particles (weight average) and has a minimum fluidization velocity, of 0.021 m at normal temperature and pressure. 

G.P.C. analysis was carried out for a series of pyrolytic oils obtained in a batch reactor operated at various temperatures similar to the P.D.U. hearth temperatures (JJ.) and the results showed a rather similar weight average molecular weight which indicate a fair selective separation of wood oil constituents at various temperatures in the P.D.U. 

Optimization of reactor operation policy is of paramount importance if improvement of product quality and increase of business profits are sought. In very specific terms, optimization of the reactor operation conditions is equivalent to producing the maximum amount of polymer product, presenting the best possible set of end-use properties, with minimum cost under safe and environmentally friendly conditions. This optimum solution is almost always a compromise. Increase of polymer productivity is usually obtained with the increase of the operational costs (increase of reactor volumes, reaction temperatures and reaction times, for instance). Besides, the simultaneous improvement of different end-use properties is often not possible (the improvement of mechanical performance is usually obtained through increase of molecular-weight averages, which causes the simultaneous increase of the melt viscosity and decrease of product processibihty). Therefore, the optimization can only be performed in terms of a relative balance among the many objectives that are pursued. 

An optimal predictive controller was developed and implemented to allow for maximization of monomer conversion and minimization of batch times in a styrene emulsion polymerization reactor, using calorimetric measiuements for observation and manipulation of monomer feed rates for attainment of control objectives [31]. Increase of 13% in monomer conversion and reduction of 28% in batch time were reported. On-line reoptimization of the reference temperature trajectories was performed to allow for removal of heater disturbances in batch bulk MMA polymerizations [64]. Temperature trajectories were manipulated to minimize the batch time, while keeping the final conversion and molecular weight averages at desired levels. A reoptimization procediue was implemented to remove disturbances caused by the presence of unknown amounts of inhibitors in the feed charge [196]. In this case, temperatiue trajectories were manipulated to allow for attainment of specified monomer conversion and molecular weight averages in minimum time. 

An optimum model-based predictive controller was developed to allow for control of molecular weight averages (intrinsic viscosities) and reactor temperatures in solid-state PET polymerizations, through manipulation of the inert gas temperatures and flowrates [ 199]. Simulation studies also showed that predictive controllers might lead to significant improvement of process operation in PVC suspension reactors, when temperatures are allowed to vary along the batch time [200]. Simulation studies performed for continuous styrene solution polymerizations showed that the closed-loop predictive control can also be used to stabilize the reactor operation at unstable open-loop steady-state conditions [201]. 

The up-flow lab reactor is modeled as a plugflow (integral) reactor. It is assumed an equilibrium between the gas and the liquid in the reactor. The temperature gradient in the reactor is small, never larger than 4-5 °C, so isothermal conditions are assumed. A weighted average of the temperature profile is used as the isothermal temperature. 

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