Validation process, model

But also inside the plastics engineering domain, the knowledge of the experts was gradually complemented by explicit process models and documented, formalized knowledge which was elaborated by the scientific community, e.g. within research projects. Therefore, with the help of experiments and computer simulation, abstract and validated process models were developed for... 

There are many processes available to validate requirements, and the reader is encouraged to refer to Validation Process Model in Fig. 12 of S AE ARP4754A. The validation process can also be facihtated by a number of tools, such as ... 

Develop via mathematical expressions a valid process or equipment model that relates the input-output variables of the process and associated coefficients. Include both equality and inequality constraints. Use well-known physical principles (mass balances, energy balances), empirical relations, implicit concepts, and external restrictions. Identify the independent and dependent variables (number of degrees of freedom). 

In a continuous reaction process, the true residence time of the reaction partners in the reactor plays a major role. It is governed by the residence time distribution characteristic of the reactor, which gives information on backmixing (macromixing) of the throughput. The principal objectives of studies into the macrokinetics of a process are to estimate the coefficients of a mathematical model of the process and to validate the model for adequacy. For this purpose, a pilot plant should provide the following ... 

Part of the confusion surrounding the model testing and validation process is largely because different meanings have been attached to the terms calibration, verification, validation, and post-audit in the technical literature. As a result of the Pellston conference, I have adopted the following relationship among these terms ... 

Figure 1 presents an overview of the model testing/valida-tion process as developed at the Pellston workshop. A distinction is drawn between validation of empirical versus theoretical models as discussed by Lassiter (4 ). In reality, many models are combinations of empiricism and theory, with empirical formulations providing process descriptions or interactions lacking a sound, well-developed theoretical basis. The importance of field data is shown in Figure 1 for each step in the model validation process considerations in comparing field data with model predictions will be discussed in a later section. 

The process of field validation and testing of models was presented at the Pellston conference as a systematic analysis of errors (6. In any model calibration, verification or validation effort, the model user is continually faced with the need to analyze and explain differences (i.e., errors, in this discussion) between observed data and model predictions. This requires assessments of the accuracy and validity of observed model input data, parameter values, system representation, and observed output data. Figure 2 schematically compares the model and the natural system with regard to inputs, outputs, and sources of error. Clearly there are possible errors associated with each of the categories noted above, i.e., input, parameters, system representation, output. Differences in each of these categories can have dramatic impacts on the conclusions of the model validation process. 

For an aquatic model of chemical fate and transport, the input loadings associated with both point and nonpoint sources must be considered. Point loads from industrial or municipal discharges can show significant daily, weekly, or seasonal fluctuations. Nonpoint loads determined either from data or nonpoint loading models are so highly variable that significant errors are likely. In all these cases, errors in input to a model (in conjunction with output errors, discussed below) must be considered in order to provide a valid assessment of model capabilities through the validation process. 

In comparing the May storms of 1978 and 1976, clearly the simulated concentration values in Figure 3 are more representative of what actually occurred than the observed values. This is not meant to be a criticism of the sampling program but an indication of how errors in observed data can exist and impact the model validation process. 

Focusing on validation process of in vitro methods, it is possible to underline some differences between tools for research and ones for toxicological testing. A research model is validated when there are some specific evidences confirming that the information from the model is able to correctly describe the process in the intact animal. Tools for toxicity testing are often used to evaluate safety hypothesis so they can be used without requiring in vivo confirmation. They are validated using a subset of well-known materials and, once validated, systems will be applied to new unknown materials or mixtures in order to evaluate their toxicity and compare their potential with other chemicals. 

Let s discuss the first requirement, a criterion for us. We notice that it is not requested that the model is validated. Validation is a formal process, which takes many years. The formal validation process of a QSAR model would end after REACH probably. 

Polymer production technology involves a diversity of products produced from even a single monomer. Polymerizations are carried out in a variety of reactor types batch, semi-batch and continuous flow stirred tank or tubular reactors. However, very few commercial or fundamental polymer or latex properties can be measured on-line. Therefore, if one aims to develop and apply control strategies to achieve desired polymer (or latex) property trajectories under such a variety of conditions, it is important to have a valid mechanistic model capable of predicting at least the major effects of the process variables. 

A valid mechanistic model can be very useful, not only in that it can appreciably add to our process understanding, but also in that it can be successfully employed in many aspects of emulsion polymerization reactor technology, ranging from latex reactor simulation to on-line state estimation and control. A general model framework has been presented and then it was shown how it can be applied in a few of these areas. The model, being very flexible and readily expandable, was further extended to cover several monomer and comonomer systems, in an effort to illustrate some of its capabilities. 

Since a valid reaction model is a prerequisite for a continuum model, the first step in any case is to construct a successful reaction model for the problem of interest. The reaction model provides the modeler with an understanding of the nature of the chemical process in the system. Armed with this information, he is prepared to undertake more complex calculations. Chapters 20 and 21 of this book treat in detail the construction of reactive transport models. 

The solution developed (see Figure 5.5) considers simultaneously, and in an optimal way, the most important aspects affecting the copper production. In order to cover the process itself and the necessary information and decision flow, the solution builds on a valid and robust process model that captures the main chemical reactions and is able to link the variable material amounts with predicted processing times. The main input data comprises ... 

In order to make the problem solvable, a linearized process model has been derived. This enables the use of standard Mixed Integer Linear Programming (MILP) techniques, for which robust solvers are commercially available. In order to ensure the validity of the linearization approach, the process model was verified with a significant amount of real data, collected from production databases and production (shift) reports. 

This chapter focuses on two main subjects. It will first deal with knowledge and methodologies of good practice in the study of chemical and microbial processes in wastewater collection systems. The information on such processes is provided by investigations, measurements and analyses performed at bench, pilot and field scale. Second, it is the objective to establish the theoretical basis for determination of parameters to be used for calibration and validation of sewer process models. These main objectives of the chapter are integrated sampling, pilot-scale and field measurements and laboratory studies and analyses are needed to determine wastewater characteristics, including those kinetic and stoichiometric parameters that are used in models for simulation of the site-specific sewer processes. 

Calibration and validation of the sewer process model (cf. Section 7.2.4). OUR measurements of corresponding upstream and downstream waste-water samples followed by a simulation (calibration) with the sewer process model. 

These four procedures are all recommended to be performed in the order shown to achieve optimal parameter estimation followed by a final validation of the gravity sewer process model (Figure 7.7). In the case of design of a new sewer system, procedure number 4 is, of course, not relevant and kinetic parameters for the sewer biofilm must be evaluated and selected based on information from comparative systems. 

Simulation procedure 4 is basically a calibration of the sewer process model for aerobic microbial transformations as described in the matrix formulation (Table 5.3). Both the biofilm processes and the reaeration are included. Initial values for the components and process parameters for this simulation originate from the sample taken at the upstream sewer station. When simulated values of the downstream COD components are acceptable, i.e., approaching the corresponding measured values, the calibration procedure is successfully completed. The major model parameters to be included in the calibration process are those relevant for the biofilm, especially km and K. After calibration, the model is ready for a successive validation process and later use in practice. 

For aerobic gravity sewers, procedure 4 is the ultimate calibration of the sewer process model. This is based on procedures 1 to 3 using information from upstream and downstream wastewater samples and by including local sewer systems and flow characteristics, temperature and DO concentration values of the wastewater in the sewer. Example 7.2 outlines the results of calibration and validation performed on a 5 km intercepting sewer line. 

Example 7.2 Calibration and validation of the sewer process model... 

Procedures 1 to4describedinSections7.2.1 through 7.2.4 are applied in this example for determination of wastewater COD fractions, model parameters and a corresponding calibration/validation of the sewer process model under aerobic and dry-weather conditions. The number of repeated tests — a total of 29 during different seasons — demonstrates not just the validity of the sewer process model depicted in Table 5.3 but also the validity of the concept behind the model formulated in Section 5.2. 

The field site used for calibration and validation of the sewer process model was an intercepting gravity sewer located between the city of Dronninglund and the wastewater treatment plant in Asaa in the northern part of Jutland, Denmark (Figure 7.11). 

FIGURE 7.12. Results for validation of the conceptual sewer process model for prediction of wastewater quality changes. Measured and simulated absolute values and changes of COD fractions for 29 dry-weather events are compared for wastewater transport in a 5.2 km gravity sewer line from Dronninglund to Asaa. 

---