Optimization processes

Optimization of (semi)batch kettle operation will include considerations of kettle time versus conversion, kettle time versus monomer recovery cost, and the potential for variations in polymerization temperature within a batch to achieve desired product properties. Open-loop trajectories may be determined 

Tirrell, M. and Gromley, K. (1981) Composition control of batch copolymerization reactors, Chem. Eng. Sci. 36, 367-75. 

(1991) Dynamics of polymerization reactors, presented at the Engineering Foundation Conf. on Polymer Reaction Engineering, Santa Barbara, California, 10-15 March. 

and Ray, W. H. (1986a) Continuous tubular polymerization reactors -1. A detailed model, Chem. Eng. Sci. 41, 3083-93. 

Chenuctd processes involve a strong interaction between mass and en gy. Typically, the overall objective of a plant is to ccmvert ami processmass. gy is used to drive reactions, effect s arations and drive pumps and compresscns. An overview of the main inputs mid outputs of a nocess is shown in Fig. 1.1. The 

In order to identify the ( timum solution, one should be able to answer the following challenging questions  

To answer the above-mentioned qu tions, one can envision so many alternatives they cannot be enumerated. Tj ically, an engineer charged with the responsibility of answering these questions examines few process options based on experience and corporate preference. Consequently, the designer develops a simulation model, performs an econmnic analysis and selects the least expensive alternative from the limited number of examined options. This solution is inappropriately designated as the c timum. Normally it is not Indeed, the true optimum may be an order of magnitude less expensive. 

It is beneflcial to consider the optimal solution for this case study shown by Fig. 1.3, with the process changes marked in thick lines, llie solution features 

The optimization component of process integration drives the iterations between synthesis and analysis toward an optimal closure. In many cases, optimization is also used within the synthesis activities. For instance, in the targeting approach for synthesis, the various objectives are reconciled using optimization. In the structure-based synthesis approach, optimization is typically the main framework for formulating and solving the synthesis task. 

As with any normal kinetic resolution, the maximum theoretical yield of chirally pure compound is 50% using the HKR technology. As the HKR of epichlorohydrin is not completely selective, process optimization consists of maximizing the isolated yield while minimizing the cycle time to obtain material of the desired ee 

Lindsey andco-workers [27,69,70], Weglarz and Atkin [32], and Metivier and co-workers [31,81] have all developed and applied Zymark robotic workstations to optimize chemistry. Lindsey and co-workers [69] completed a factorial design study (16 experiments) to examine the role of catalyst and reactant concentrations on porphyrin yield in less than 1 day of workstation time. Weglarz and Atkin at Dow Chemical Company [32] studied the effect of reaction parameters on (i) the alkoxy substitution of cellulosic ethers (ii) the base-catalyzed conversion ofphenethyl bromide to styrene and (iii) the onset of crystallization employing a fiber optic probe. Metivier and co-workers at Rhone-Poulenc [31,81] focused on the evaluation of catalysts, reagents, and solvents for process optimization work of numerous proprietary reactions. 

Boettger [84] developed and implemented a robotic system for the optimization of a palladium-catalyzed reaction by varying the concentration, temperature, and amount and type of catalyst and additives. Porte and co-workers have developed and applied numerous systems for automated process optimization. Representative reactions include the 

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Preliminary process optimization is greatly simplified, as will be seen in the next chapter. 

In batch process optimization, one of the principal objectives is to improve equipment utilization through reduction in dead time. This requires both structural and parameter optimization, with many options available. 

Preliminary process optimization. Dominant process variables such as reactor conversion can have a major influence on the design. Preliminary optimization of these dominant variables is often required. 

To evaluate design options and carry out preliminary process optimization, simple economic criteria are required. What happens to the revenue from product sales after the process has been commissioned The sales revenue first pays for fixed costs which are independent of the rate of production. Variable costs, which do depend on the rate of production, also must be met. After this, taxes are deducted to leave the net profit. 

The examples discussed in tliis chapter show a strong synergy between fundamental physical chemistry and device processing metliods. This is expected only to become richer as shrinking dimensions place ever more stringent demands on process reliability. Selecting key aspects of processes for fundamental study in simpler environments will not only enable finer control over processes, but also enable more sophisticated simulations tliat will reduce tire cost and time required for process optimization. 

The first-stage catalysts for the oxidation to methacrolein are based on complex mixed metal oxides of molybdenum, bismuth, and iron, often with the addition of cobalt, nickel, antimony, tungsten, and an alkaU metal. Process optimization continues to be in the form of incremental improvements in catalyst yield and lifetime. Typically, a dilute stream, 5—10% of isobutylene tert-huty alcohol) in steam (10%) and air, is passed over the catalyst at 300—420°C. Conversion is often nearly quantitative, with selectivities to methacrolein ranging from 85% to better than 95% (114—118). Often there is accompanying selectivity to methacrylic acid of an additional 2—5%. A patent by Mitsui Toatsu Chemicals reports selectivity to methacrolein of better than 97% at conversions of 98.7% for a yield of methacrolein of nearly 96% (119). 

Sulfochlorination of Paraffins. The sulfonation of paraffins using a mixture of sulfur dioxide and chlorine in the presence of light has been around since the 1930s and is known as the Reed reaction (123). This process is made possible by the use of free-radical chemistry and has had limited use in the United States. Other countries have had active research into process optimization (124,125). 

Mineral acids are used as catalysts, usually in a concentration of 20— 40 wt % and temperatures of 30—60°C. An efficient surfactant, preferably one that is soluble in the acid-phase upon completion of the reaction, is needed to emulsify the a-pinene and acid. The surfactant can then be recycled with the acid. Phosphoric acid is the acid commonly used in the pine oil process. Its mild corrosion characteristics and its moderate strength make it more manageable, especially because the acid concentration is constandy changing in the process by the consumption of water. Phosphoric acid is also mild enough to prevent any significant dehydration of the alcohols formed in the process. Optimization of a process usually involves considerations of acid type and concentration, temperature, surfactant type and amount, and reaction time. The optimum process usually gives a maximum of alcohols with the minimum amount of hydrocarbons and cineoles. 

A future goal for the integration of graphics and process design simulators is to be able to use an interactive graphics program to prepare the input to the process simulator. This capabiHty would allow tme on-line process modification, flow-sheet optimization, and process optimization, and is likely to be one of the key developments in this field in the 1990s (99). 

Historically, sequential-modular simulators were developed first. They were also developed primarily ia iadustry. They coatiaue to be widely used. la terms of unit operatioas, each module can be made as simple or complex as needed. New modules can be added as needed. Equation-oriented simulators, on the other hand, are able to handle arbitrary specifications and limitations for the entire process dow sheet more dexibly and conveniendy than sequential-modular simulators, and process optimization can also be carried out with less computer effort. 

This code is iavoked for the process optimization problem oace it is formulated as a quadratic problem locally. The solutioa from the code is used to arrive at the values of the optimization variables, at which the objective fuactioa is reevaluated and a new quadratic expression is generated for it. The... 

Spreadsheet Applications. The types of appHcations handled with spreadsheets are a microcosm of the types of problems and situations handled with fuU-blown appHcation programs that are mn on microcomputers, minis, and mainframes and include engineering computations, process simulation, equipment design and rating, process optimization, reactor kinetics—design, cost estimation, feedback control, data analysis, and unsteady-state simulation (eg, batch distillation optimization). 

D. A. Sofge and D. A. White, "Neural Network Based Process Optimization and Control," in Proceedings of the 29th Conference on Decision and Control,... 

While process design and equipment specification are usually performed prior to the implementation of the process, optimization of operating conditions is carried out monthly, weekly, daily, hourly, or even eveiy minute. Optimization of plant operations determines the set points for each unit at the temperatures, pressures, and flow rates that are the best in some sense. For example, the selection of the percentage of excess air in a process heater is quite critical and involves a balance on the fuel-air ratio to assure complete combustion and at the same time make the maximum use of the Heating potential of the fuel. Typical day-to-day optimization in a plant minimizes steam consumption or cooling water consumption, optimizes the reflux ratio in a distillation column, or allocates raw materials on an economic basis [Latour, Hydro Proc., 58(6), 73, 1979, and Hydro. Proc., 58(7), 219, 1979]. 

Unconstrained Optimization Unconstrained optimization refers to the case where no inequahty constraints are present and all equahty constraints can be eliminated by solving for selected dependent variables followed by substitution for them in the objec tive func tion. Veiy few reahstic problems in process optimization are unconstrained. However, it is desirable to have efficient unconstrained optimization techniques available since these techniques must be applied in real time and iterative calculations cost computer time. The two classes of unconstrained techniques are single-variable optimization and multivariable optimization. 

In this case, there are n process variables with equality constraints and inequahty constraints. Such problems pose a serious challenge to performing optimization calculations in a reasonable amount of time. Typical constraints in chemical process optimization include operating conditions (temperatures, pressures, and flows have limits), storage capacities, and produc t purity specifications. 

Shenoy, U. V. (1995). Heat Exchange Network Synthesis Process Optimization by Energy and Resource Analysis. Gulf Phb. Co., Houston, TX. 

The reasons for measuring these streams are process optimization, mandated regulatory information, analyzer application development, and identification of process or effluent abnormalities.-... 

A thinking process optimizing system performance. It examines the system and focuses on the constraints that limit overall system performance. It looks for the weakest link in the chain of processes that produce organizational performance and seeks to eliminate it and optimize system performance. 

The first modeling software which allowed for the optimization of nonlinear separations by SMB was presented in the early 1990s [46]. Today, numerous publications from academia allows one to have a better understanding of the SMB system [47-51]. Industry now has the practical tools for modeling SMB for quick and efficient process optimization [41, 52]. 

Perhaps the most important role of the plant engineer is process optimization. This function has the sole responsibility for improving the reliability and performance of production and auxiliary systems. 

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