Defining research problem

Experimental research of the system must be preceded by preliminary examination of the subject of research aimed at obtaining information necessary for defining the research objective. 

The research objective may be defined if the research subject or optimization subject is defined, if its requirements are known and if there exist interactions that change the quality of a research subject with the change of requirements. 

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- 

Input variables are controllable, uncontrollable and disturbance variables. Controllable variables or factors X1 X2. X are variables, that can be directed or that can affect the research subject in order to change the response. They can be numerical (example temperature) or categorical (example raw material supplier). Uncontrollable variables Z1 Z2. Zp are measured and controlled during the experiment but they cannot be changed at our wish. They can be a major cause for variability in the responses. Other sources of variability are deviations around the set points of the controllable factors, plus sampling and measurement error. Furthermore, the system itself may be composed of parts that also exhibit variability. Disturbance, non controlled variables Wi, W2. Wq are immeasurable and their values are randomly changed in time. 

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 

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Research that is aiming to be policy-relevant will inevitably have a focus on a real-world problem. This is a necessary distinction because academic disciplines often define research problems in a narrow way, leading to research that is abstract and possibly irrelevant to society s pressing challenges. 

Methodology. Practitioners of chemical market research develop iadividual styles and techniques. However, four elements are essential to every useful study defining the problem, data gathering, analysis of data, and presentation of findings. 

In later chapters, I analyze two broadly defined research schools, one in France and another in England, and their roles in the development of the discipline of a theoretical chemistry distinct from physical chemistry and theoretical physics. One group, which I call the Paris school, established the field of theoretical chemistry at the Ecole Normale Superieure. It was allied with organic chemistry, on the one hand, and physical chemistry, on the other. The second school, which I call the London-Manchester school, similarly combined problems and approaches from organic and physical chemistry but more daringly dabbled in the physics of electron theory and quantum mechanics. Thus, the discipline of theoretical chemistry took different forms in the two national traditions. 

In combinatorial-type materials studies - as in all research problems - suitable constraints must be identified to yield a tractable experimental space for investigation. These constraints can include a processing window and limitations of the elements investigated. For example, some metals might be too expensive for a particular end use. By reducing the number of experiments to a suitably small number, then exhaustive understanding of a particular system becomes feasible. In any proposed combinatorial study it is therefore critical to first establish a clear vision of the goal of the study, so that parameters of the study can be defined and constrained. 

Writing a research proposal is a good way for you to learn how chemists work—how they define meaningful problems, design experimental approaches to investigate these problems, and anticipate how their research will contribute reliable scientific knowledge to the community of chemists. 

Much of the discussion in this chapter is from the point of view of a consultant (chemometrician) working with a project sponsor. However, the concepts discussed here can also be applied if the chemometrician is an independent researcher. As a consultant, the chemometrician aids in defining the problem, planning and executing experiments, and analyzing the data. 

Research problems with one response undoubtedly have an advantage. In practice, however, we mostly meet research subjects with several responses, which often means a literally large number of responses. Thus, for example, when producing rubber, plastic and other composite materials one must take into account responses such as physical-chemical, technological, economic, mechanical (tensile strength, elongation, module, etc.) and others. One can define the mathematical model for each of the mentioned responses but simultaneous optimization of several functions is mathematically impossible. 

Factor concordance is a property that makes it possible for all factor combinations to be realized in an experiment. This property is very important when an experiment with several simultaneous factor variations is designed. It is not a rare case where the lack of this property brings about a change in defining a research problem, excluding some factors from the experiment, or it changes the domain of factors. 

Synthesis is the step in design where one conjectures the building blocks and their interconnection to create a structure which can meet stated design requirements. This review paper first defines chemical process synthesis and indicates the nature of the research problems—to find representations, evaluation functions and search strategies for a potentially nearly infinite problem. It then discusses synthesis research and the most significant results in each of six areas—heat exchanger networks, separation systems, separation systems with heat integration, reaction paths, total flowsheets and control systems. 

The expert, a senior research chemist for example, defines a problem domain and injects some of his knowledge about that particular area into the KEY system. KEY analyzes this knowledge and provides the expert with automated techniques to isolate and eliminate inconsistencies and ambiguities. Once these are removed, the knowledge is converted into rules. These rules are the power that drives the inference engine. The result of... 

Once you know the function of your paper and have identified its audience, review your material for completeness or excess. Then, organize your material into the standard format introduction, experimental details or theoretical basis, results, discussion, and conclusions. This format has become standard because it is suitable for most reports of original research, it is basically logical, and it is easy to use. The reason it accommodates most reports of original research is that it parallels the scientific method of deductive reasoning define the problem, create a hypothesis, devise an experiment to test the hypothesis, conduct the experiment, and draw conclusions. Furthermore, this format enables the reader to understand quickly what is being presented and to find specific information easily. This ability is crucial now more than ever because scientists, if not all professionals, must read much more material than their time seems to allow. 

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