Black box experiments

The development and optimization of chemical EOR systems can best be achieved on the basis of a good understanding of the mechanisms by which oil is displaced from a porous matrix, and of the parameters which control those mechanisms. Better insight into the displacement processes is required than is afforded by "black box" experiments such as displacement tests in sand packs. It is therefore necessary to carry out laboratory experiments concerned with specified system parameters, and to link the results of these experiments with displacement tests. The most commonly applied measurement used in this way is of interfacial tension, which is linked to displacement tests via intuitive conceptual models of the displacement process. Only relatively recently have systematic studies been undertaken (5) to test these models. 

Determine "best conditions"via statistical black box experiments... 

Figure 23 Black box for observation of the energy transfer and energy migration demonstration experiments (1) Mag-Lite A A flashlight, (2) Schott DAD 8-1 interference filter at 486.7 5 nm, (3) sample, (4) Schott OG 515 cutoff filter.

It must always be remembered that optimisation is not an exact science and, therefore, it is sometimes difficult to define confidence limits in the final optimised values for the coefficients used in the thermodynamic models. The final outcome is at least dependent on the number of experimental measurements, their accuracy and the ability to differentiate between random and systematic errors. Concepts of quality can, therefore, be difficult to define. It is the author s experience that it is quite possible to have at least two versions of an optimised diagram with quite different underlying thermodynamic properties. This may be because only experimental enthalpy data were available and different entropy fiinctions were chosen for the different phases. Also one of the versions may have rejected certain experimental measurements which the other version accepted. This emphasises the fact that judgement plays a vital role in the optimisation process and the use of optimising codes as black boxes is dangerous. 

Hepatic events Experience with long-term therapy with danazol is limited. Peliosis hepatis and benign hepatic adenoma have been observed with long-term use (see Black..Box.WamlDfl). 

We shall subsequently use the MATLAB ODE solvers as black boxes, sometimes varying between the individual ones only for higher speed or better accuracy when warranted. Our students should experiment freely with using either of the above seven ODE solvers to learn which is more advantageous where. It only takes a different call of MATLAB to find out. 

The next step is choice of research subject model. It has been said before that design of experiments rests on cybernetic concepts about the research subject. A black box> is therefore recommended as the research subject model, which will be affected by various controllable factors. The defining principles of such a model cor-... 

Factors may have associated values called levels of variations. Each state of a black box has a definite combination of factor levels. The more different states of the black box that exist, the more complex is the research subject. Formalization of preliminary information includes analysis of reference data, expert opinions and use of direct data, which enables correct selection of response, factors and null point or center of experiment. Factor limitations are also defined at this stage. If the research is linked with several following responses, then response limitations also have to be analyzed. The next phase refers to defining the research problem. When defining this problem one must keep in mind the research-subject model, and in a general case it is Eq. (2.1) that defines the link between the inlet and outlet of the black box. Defining the research problem is possible only now when its aim has been determined, the criteria established, the factors, limitations and null point defined. The problem is a simple one when only one response or optimization criterion is in... 

In practice we are often faced with a research subject that has several technological phases and where the response is measured in its last phase. In that case, the subject is studied cybernetically as a black box , like a unique technological phase with all the factors that corresponded to individual technological phases. We had no responses by individual technological phases in this case, but this may occur. Moreover, response optima by individual phases contradict the general optimum system. This indicates that optimization by individual phases of a research subject is justified and possible. In this way it is possible to incorporate into the design of an experiment, factors from various phases of a research subject, but this is not always necessary. 

Analytical gradients and Hessians are available for CASSCF, and it is expected that this technology will be extended to the MR-CI and MP2 methods soon. Further, by virtue of the multireference approach, a balanced description of ground and excited states is achieved. Unfortunately, unlike black boxes such as first-order response methods (e.g., time-dependent DFT), CAS-based methods require considerable skill and experience to use effectively. In the last section of this chapter, we will present some case studies that serve to illustrate the main conceptual issues related to computation of excited state potential surfaces. The reader who is contemplating performing computations is urged to study some of the cited papers to appreciate the practical issues. 

As a general approach in RSM, one uses the black box principle (see Figure 8.1a). According to this principle, any technological process can be characterized by its input variables, xh i= I,. .., the output or response variables, yb i - 1,. .., s and the noise variables, w i= I,. .., Z. One then considers two ways of performing an experiment, active or passive. There are several important presumptions for active experiments ... 

We view the real or the simulated system as a black box that transforms inputs into outputs. Experiments with such a system are often analyzed through an approximating regression or analysis of variance model. Other types of approximating models include those for Kriging, neural nets, radial basis functions, and various types of splines. We call such approximating models metamodels other names include auxiliary models, emulators, and response surfaces. The simulation itself is a model of some real-world system. The goal is to build a parsimonious metamodel that describes the input-output relationship in simple terms. 

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