Process development, goals

Equation (2.14) has the advantage of simplicity its drawback is that we learn nothing about either the nature of viscosity or the nature of the sample from the result. In the next few sections we shall propose and develop a molecular model for the flow process. The goals of that development will be not only to describe the data, but also to do so in terms of parameters which have some significance at the molecular level. Before turning to this, it will be helpful if we consider a bit further the form of Eq. (2.14). 

However, even when we determine that the benefits of an inherently less safe technology justify its use, we should always continue to look for inherently safer alternatives. Technology continues to evolve and advance, and inherently safer alternatives which are not economically attractive today may be very attractive in the future. The development of new inherently safer technology offers the promise of more reliably and economically meeting process safety goals. 

Specific goals and objectives will vary from plant to plant. However, we will provide an example that will illustrate the process. Before goals and objectives can be developed for your plant, you must determine the existing maintenance costs and other parameters that will establish a reference or baseline dataset. Since most plants do not track the true cost of maintenance, this may be the most difficult part of establishing a predictive maintenance program. 

The ultimate goal of process development is to achieve feasibility where it is possible to produce amino adds on a large scale at a production cost per kg of amino add comparable to, or cheaper than, the processes currently used by other companies. If we presume that the technical performance (fermentation and recovery) are sorted out on a laboratory scale and scaling up looks promising, then it is time to find out whether it is possible to operate economically on a large scale. 

In order to achieve this goal of a fully integrated process sequence, a concerted research and process development effort must take place. Present R D efforts are devoted to the development of cost-effective pyrochemical processes for the recycle of plutonium in residues. Future efforts will be aimed at the recycle of reagents in each individual process. The objectives of the recycle are to produce plutonium metal which can be further purified, and to generate small volumes of residues which can be discarded or recycled. 

The pitfalls of a computer model are obvious in that it is only a conceptual representation of the reactor and includes only as many aspects of the real reactor as present knowledge permits. In addition, even the most perfectly conceived description will still depend upon the accuracy of the physically measured constants used in the model for the quality of the process representation. The goal of this report is, however, only to show conceptual trends and the technological base is developed to the extent that the conceptual trends will be correct. In some respects the computer model is a better process development tool than the pilot plant used for the LDPE process since the pilot reactor does not yield directly scaleable information. The reader should take care to direct his attention to the trend information and conceptual differences developed in this work very little attention should be paid to the absolute values of the parameters given. 

IBM s definition of mentoring - for example, Mentoring is a process by which an individual (mentee) strives to achieve development goals under the guidance of another individual with special expertise (mentor). ... 

The acquisition of mentoring skills, techniques and processes can enable individuals to help many people reach their development goals. 

Application of LCA in process development - a case study 7.3.1 Goal and scope of the case study 1... 

The specific application of a laboratory reactor determines its shape and size, and the degree of sophistication in design. However, no matter which application is under consideration, the final goal in process development is always the highest product yield and the lowest yields of side products, the highest activity of the reaction system (reactants + solvent + catalyst), the lowest cost, and stable and safe operation. 

To support preparation of multiple kilograms of compound 1 and develop a route that is potentially suitable for long term needs to supply much larger amounts, a few goals were set for the process development of 1 after analyzing the synthetic... 

The subject of this chapter is broad and it is possible to discuss only the simpler—though fundamental—aspects, using examples that are representative. The goal is to provide the reader with the necessary insight to engage in solvent extraction research and process development with good hope of success. 

The goal of this chapter is to provide a brief overview of standard engineering methods for process development and scale-up and discuss their applicability to the pharmaceutical industry. Model-based design methods and their impact on optimization, scale-up, and process control are discussed. The state of the art is contrasted to a realistic desirable state where these methods become part of a new standard of technological articulation. 

Fortunately, as this chapter is written in January 2005, product and process development in the pharmaceutical industry appears to be entering a period of deep transformation, initially driven by recognition at the FDA that a higher technological standard was a desirable and achievable goal, and fueled by an intense desire for improvement on the part of many industrial scientists and engineers. 

When scaling-up the fluid-bed process, a major requirement is to produce fluidization behavior on the larger machines equivalent to that used on the scale that provided the basis for process development. To achieve this goal, and minimize attritional effects, the same air velocities for each scale of equipment are required. Thus, the overall increase in air volume required during scale-up will be related to the increase in area of the perforated base plate, and, in the case of the Wurster process, the open area of the partition plate immediately beneath each of the inner partitions. Such calculations are simplified when scaling-up from an 18" pilot scale machine to, say, a 32" machine, since the latter represents a three-multiple of the former, and thus would require a threefold increase in airflow. 

Transforming disparate processes into processes that are simple to understand, easy to execute, and provide a sense of accomplishment meet one of management s obligations to staff. Staff interest lies in the ability to perform their work, contribute to continuous improvement, and have a reasonable work-life balance. Finally, they want to be able to contribute to their careers, have defined career paths, and have attainable development goals for advancement. A well-designed quality management system can contribute to provide all these employee benefits. 

Develop and execute communication plan Initial and ongoing training Facilitate management review process Identify process maturity goals and metrics Develop long-term strategic vision Create and execute annual action plan... 

There are multiple paths to achieving the state when a product and a process are in control. A pictorial representation of this concept is shown in Figure 1.2. Simpler molecules may achieve a state of control (predictable state) early in the development process, while more complex molecules may retain a high state of variability until late in the process. The goal for development must be a development path that is documented and performed by qualified scientists, equipment, facilities, instruments, etc. Development paths that can be followed are varied, but the final outcome, when a project is transferred to manufacturing, is a product and a process that are in a well-characterized state of control. 

In an abstract sense, parameterization can be a very well-defined process. The goal is to develop a model that reproduces experimental measurements to as high a degree as possible. Thus, step 1 of parameterization is to assemble the experimental data. For molecular mechanics, these data consist of structural data, energetic data, and, possibly, data on molecular electric moments. We will discuss the issues associated with each kind of datum further below, but for the moment let us proceed abstractly. We next need to define a penalty function , that is, a function that provides a measure of how much deviation there is between our predicted values and our experimental values. Our goal will then be to select force-field parameters that minimize the penalty function. Choice of a penalty function is necessarily completely arbitrary. One example of such a function is... 

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