Process optimization techniques

In this case all three stages of the process were greened by using conventional process optimization techniques no new or even leading-edge chemistry had been developed. 

Optimization techniques are used to find either the best possible quantitative formula for a product or the best possible set of experimental conditions (input values) needed to run the process. Optimization techniques may be employed in the laboratory stage to develop the most stable, least sensitive formula, or in the qualification and validation stages of scale-up in order to develop the most sta-... 

While the purpose of Design Scorecards is to prevent problems, defects, and errors through superior design, they also enable better problem detection after a new solution (design) is implemented. If you are in detect-and-fix mode, any number of process-optimization techniques may help, such as Process Behavior Charts (Technique 52), Cause Effect Matrix (Technique 54), Mistake Proofing (Technique 49), and Design of Experiments (Technique 50). 

An applicable example of optimization, which was derived from dehydration of a model dehydrated food, has been described by Mishkin et al. (1982). They suggested that food engineering has lagged behind other engineering disciplines in implementing process optimization techniques... 

Godat, J. and Marechal, F. (2003) Optimization of a fuel cell system using process optimization techniques. J. 

The descriptor set can then be reduced by eliminating candidates that show such bad characteristics. Optimization techniques such as genetic algorithms (see Section 9.7) are powerful means of automating this selection process. 

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. 

As has been discussed in Chapter One, mathematical programming (or optimization) is a powerful tool for process integration. For an overview of c mization and its application in pollution prevention, the reader is referred to El-Halwagi (1995). In this chapter, it will be shown how optimization techniques enable the designer to ... 

We are now in a position to solve the pharmaceutical case study (Section 9.1.2) using optimization techniques. The first step is to create the TID including process streams and utilities (Fig. 9.15). Next, the problem is formulated as an optimization program as follows ... 

The analytical tools to accomplish laminate design are at least twofold. First, the invariant laminate stiffness concepts developed by Tsai and Pagano [7-16 and 7-17] used to vary laminate stiffnesses. Second, structural optimization techniques as described by Schmit [7-12] can be used to provide a decision-making process for variation of iami-nate design parameters. This duo of techniques is particularly well suited to composite structures design because the simultaneous possibility and necessity to tailor the material to meet structural requirements exists to a degree not seen in isotropic materials. 

While it is perfectly permissible to estimate a and b on this basis, the calculation can only be done in an iterative fashion, that is, both a and b are varied in increasingly smaller steps (see Optimization Techniques, Section 3.5) and each time the squared residuals are calculated and summed. The combination of a and b that yields the smallest of such sums represents the solution. Despite digital computers, Adcock s solution, a special case of the maximum likelihood method, is not widely used the additional computational effort and the more complicated software are not justified by the improved (a debatable notion) results, and the process is not at all transparent, i.e., not amenable to manual verification. 

Latour, P., Use of steady-state optimization for computer control in the process industries. In On-line Optimization Techniques in Industrial Control (Kompass, E. J. and Williams, T. J., eds.). Technical Publishing Company, 1979. 

In fact, no model can represent every aspect of an actual production process. Accordingly, the. scheduler must have some flexibility to modify the schedule proposed by the optimization algorithm, based on experience that is gained al.so at the realization of the optimal schedule. This leads to evolutionary improvement strategies starting from approximate optimization techniques. An interactive graphical presentation of the plant should enable quick intervention. 

Sample preparation, injection, calibration, and data collection, must be automated for process analysis. Methods used for flow injection analysis (FLA) are also useful for reliable sampling for process LC systems.1 Dynamic dilution is a technique that is used extensively in FIA.13 In this technique, sample from a loop or slot of a valve is diluted as it is transferred to a HPLC injection valve for analysis. As the diluted sample plug passes through the HPLC valve it is switched and the sample is injected onto the HPLC column for separation. The sample transfer time typically is determined with a refractive index detector and valve switching, which can be controlled by an integrator or computer. The transfer time is very reproducible. Calibration is typically done by external standardization using normalization by response factor. Internal standardization has also been used. To detect upsets or for process optimization, absolute numbers are not always needed. An alternative to... 

In the last few years, optimization techniques have become more widely used in the pharmaceutical industry. Some of these have appeared in the literature, but a far greater number remain as in-house information, using the same techniques indicated in this chapter, but with modifications and computer programs specific to the particular company. An excellent review of the application of optimization techniques in the pharmaceutical sciences was published in 1981 [20]. This covers not only formulation and processing, but also analysis, clinical chemistry, and medicinal chemistry. 

Other applications of the previously described optimization techniques are beginning to appear regularly in the pharmaceutical literature. A literature search in Chemical Abstracts on process optimization in pharmaceuticals yielded 17 articles in the 1990-1993 time-frame. An additional 18 articles were found between 1985 and 1990 for the same narrow subject. This simple literature search indicates a resurgence in the use of optimization techniques in the pharmaceutical industry. In addition, these same techniques have been applied not only to the physical properties of a tablet formulation, but also to the biological properties and the in-vivo performance of the product [30,31]. In addition to the usual tablet properties the authors studied the following pharmacokinetic parameters (a) time of the peak plasma concentration, (b) lag time, (c) absorption rate constant, and (d) elimination rate constant. The graphs in Fig. 15 show that for the drug hydrochlorothiazide, the time of the plasma peak and the absorption rate constant could, indeed, be... 

Ignacio E. Grossmann, Mixed-Integer Optimization Techniques for Algorithmic Process Synthesis... 

Like any businesses, bioanalytical laboratories perform operations that transform starting materials (samples and supplies) into products of higher value (quality reports continuing accurate sample concentration data). To maximize productivity and stay ahead of competition, bioanalytical scientists continuously invent, reinvent, and implement processes and techniques that generate more accurate and better quality reports with fewer resources (labor, time, capital, energy, and consumable goods). These continuous optimizations of laboratory operations drove the bioanalytical laboratories to begin... 

Optimization is the use of specific methods to determine the most cost-effective and efficient solution to a problem or design for a process. This technique is one of the major quantitative tools in industrial decision making. A wide variety of problems in the design, construction, operation, and analysis of chemical plants (as well as many other industrial processes) can be resolved by optimization. In this chapter we examine the basic characteristics of optimization problems and their solution techniques and describe some typical benefits and applications in the chemical and petroleum industries. 

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