Design process objectives

Feed characteri2ation, particularly for nondesalination appHcatioas, should be the first and foremost objective in the design of a reverse osmosis plant. This involves the determination of the type and concentration of the main solutes and foulants in the stream, temperature, pH, osmotic pressure, etc. Once the feed has been characteri2ed, a reaHstic process objective can be defined. In most cases, some level of pretreatment is needed to reduce the number and concentration of foulants present in the feed stream. Pretreatment necessitates the design of processes other than the RO module, thus the overaH process design should use the minimum pretreatment necessary to meet the process objective. Once the pretreatment steps have been determined and the final feed stream defined, the RO module can be selected. 

Prediction of reverse osmosis performance is usefiil to the design of RO processes. Simulation of RO processes can be separated iato two categories. The first is the predictioa of membrane module performance. The second is the simulation of a network of RO processes, ie, flow sheet simulations, which can be used to determine the optimum placement of RO modules to obtain the overaH process objective. 

Given the first type of simulation, it is advantageous to be able to design a system of RO modules that can achieve the process objective at a minimal cost. A model has been iategrated iato a process simulation program to predict the stream matrix for a reverse osmosis process (132). In the area of waste minimization, the proper placement of RO modules is essential for achieving minimum waste at a minimum cost. Excellent details on how to create an optimal network of RO modules is available (96). 

The creative part of the design process is the generation of possible solutions to the problem (ways of meeting the objective) for analysis, evaluation and selection. In this activity the designer will largely rely on previous experience, his own and that of others. 

Snapshots illustrate specific example situations. Figure 6.18 shows snapshots depicting the state of our spreadsheet before and after an operation. (The thicker lines and bold type represent the state after the operation.) Notice that because we are dealing with a requirements model here, we show no messages (function calls) between the objects they will be decided in the design process. Here we re concerned only with the effects of the operation invoked by the user. This is part of how the layering of decisions works in Catalysis We start with the effects of operations and then work out how they are implemented in terms of collaborations between objects. 

The aim of this section is to provide a generic step by step methodology for the design of a final purification process for a non-salt form API using crystallization. The process objective is to consistently manufacture API of the desired purity and polymorphic form, within the constraints of a typical batch production facility. A brief outline of the analytical techniques that may be required is presented in section 4.6. 

A clinical trial is an experiment and not only do we have to ensure that the clinical elements fit with the objectives of the trial, we also have to design the trial in a tight scientific way to make sure that it is capable of providing valid answers to the key questions in an unbiased, precise and structured way. This is where the statistics comes in and statistical thinking is a vital element of the design process for every clinical trial. 

To ensure the successful design of a reverse osmosis process, several factors should be considered. These considerations encompass the feed solution, the membrane module, and the use of other processes in the pre- and post-treatment processes. A thorough knowledge of the feed stream and its components is essential to the prevention of membrane damage and product impurities. Once the feed stream is characterized and the process objective is defined, design can be initiated. 

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