Process variables on product

For practical use, combination charts (12 > 20) have been prepared which show the effect of the above process variables on product distribution and also the interrelationships of the yields of the various products. 

The effects of the dependent process variables on product characteristics The converse effects of product characteristics on dependent process variables... 

J. R. Influence of some process variables on product properties for a hydrophilic matrix controlled release tablet. Eur. J. Pharm. Biopharm. 1992, 38 (3), 113-118. 

Predict the effect of changes in process variables on product. 

Patent activity is very aggressive in the personal and household care detergent industry, based on the total number of worldwide patents issued annually. A review of the current patent literature highlights the complexity of liquid detergent compositions and their manufacturing requirements. In process technology, the influence of process variables on product efficacy, stability, and viscosity control is common patent subject matter, disclosed for both structured and unstructured systems. 

Uddin MS, Hawlader MN, Zhu HJ. Microencapsulation of ascorbic acid Effect of process variables on product characteristics. Journal of Microencapsulation. March-April 2001 18(2) 199-209. PubMed PMID 11253937. 

The effects of the dependent process variables on product characteristics... 

A review is given of studies of reactions in ionic solid systems and of the implications of these studies for industrial applications. Work on the kinetics of solid-state reaction systems is discussed, as are studies of reaction mechanisms and of the effects of process variables on product characteristics. As examples of the significance of these studies for industry the formation of ferrites and of other spinels by reaction in the solid state, the use of catalytic processes employing such solid catalysts as zeolites, and the development of batteries and fuel cells using solid-state electrolytes are described. 

In order to understand potential problems and solutions of design, it is helpful to consider the relationships of machine capabilities, plastics processing variables, and product performance (Fig. 1-10). A distinction has to be made here between machine conditions and processing variables. For example, machine conditions include the operating temperature and pressure, mold and die temperature, machine output rate, and so on. Processing variables are more specific, such as the melt condition in the mold or die, the flow rate vs. temperature, and so on (Chapter 8). 

This section is divided into three parts. The first is a comparison between the experimental data reported by Wisseroth (].)for semibatch polymerization and the calculations of the kinetic model GASPP. The comparisons are largely graphical, with data shown as point symbols and model calculations as solid curves. The second part is a comparison between some semibatch reactor results and the calculations of the continuous model C0NGAS. Finally, the third part discusses the effects of certain important process variables on catalyst yields and production rates, based on the models. 

For reactor design purposes, the distinction between a single reaction and multiple reactions is made in terms of the number of extents of reaction necessary to describe the kinetic behavior of the system, the former requiring only one reaction progress variable. Because the presence of multiple reactions makes it impossible to characterize the product distribution in terms of a unique fraction conversion, we will find it most convenient to work in terms of species concentrations. Division of one rate expression by another will permit us to eliminate the time variable, thus obtaining expressions that are convenient for examining the effect of changes in process variables on the product distribution. 

Davies, W.L. and Gloor, W.T., Batch production of pharmaceutical granulations in a fluidized bed, I. Effects of process variables on physical properties of final granulation, /. Pharm. Sci., 60 (1971) 1869-1874. 

TABLE 2-2. Effect of Process Variables on Thermal Cracking Unit Products... 

The air oxidation of 2-methylpropene to methacrolein was investigated at atmospheric pressure and temperatures ranging between 200° and 460°C. over pumice-supported copper oxide catalyst in the presence of selenium dioxide in an integral isothermal flow reactor. The reaction products were analyzed quantitatively by gas chromatography, and the effects of several process variables on conversion and yield were determined. The experimental results are explained by the electron theory of catalysis on semiconductors, and a reaction mechanism is proposed. It is postulated that while at low selenium-copper ratios, the rate-determining step in the oxidation of 2-methylpropene to methacrolein is a p-type, it is n-type at higher ratios. 

The main objective of the present work was the production of ethyl esters from the enzymatic alcoholysis of castor oil using n-hexane as solvent. Two commercial lipases, Novozym 435 and Lipozyme IM, were compared. The variables in these experiments were temperature, water and enzyme concentrations in the reaction medium, and the oibethanol molar ratio. An empirical model was built to evaluate the effects of process variables on the conversion, and thus to determine the operating conditions that maximize the production of esters for each enzyme. 

Hahn, J.J., Ghirardi, M.L. and Jacoby, W.A. 2004. Effect of process variables on photosynthetic algal hydrogen production. Biotechnol. Progr. 20, 989-991. 

THE EFFECT OF THE MAIN PROCESS VARIABLES ON THE YIELD AND COMPOSITION OF PYROLYSIS PRODUCTS... 

Chapman SR. Influence of process variables on the production of spherical granules. PhD thesis, University of London, 1985. 

For continuous processes operating at steady state, optimization typically consists in determining the operating point that minimize or maximize some performance of the process (such as minimization of operating cost or maximization of production rate), while satisfying a number of constraints (such as bounds on process variables or product specifications). In mathematical terms, this optimization problem can be stated as follows ... 

Here the critical process variables are identified from the selected list of process variables. The model library or process data (if available) are used for this analysis. To perform the sensitivity analysis, the process operational model is simulated through ICAS-MoT. The effect of each process variable on the target product properties is analyzed systematically through open loop simulation. The operational objectives have to be assessed first. If an operational objective is not achieved, then the process variables have to be analyzed. The variables which violate the operational limit and have a major effect on the product quality are considered as the critical process variables. For some of the variables which can not be modeled the sensitivity analysis has to be performed qualitatively through inference from the knowledge base and/or by the use of process data. All the critical process variables need to be monitored and controlled. For some of the critical variables that can not be measured in real time, other correlated properties have to be measured so that all critical variables can be measured and controlled by using the correlations to the measurable variables. 

Particleboard utilizes residue materials from other wood processing operations. Sawdust and shavings predominate, although most plants are able to use chips or flakes made from roundwood to meet specific strength requirements in the product. Cost is the major driver, with the proportion of high cost material such as chips or roundwood limited to that necessary to meet product requirements. Particle size distribution and shape have become more important as the effect of these variables on product quality has been recognised, leading to reductions in cost. 

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