Mirko Dohnal

Fuzzy food engineering

Abstract A classical quantitative (analytical and/or statistical) analysis is not appropriate for some ill defined and/or very complex food engineering problems. Therefore, a new form of analysis using fuzzy mathematics has been applied. The fuzzy model can utilize data which are to a certain level inconsistent. To minimize an information loss of valuable knowledge, conventional pre-processing (usually statistical analysis) of this knowledge is eliminated. Primary knowledge (e.g. experimental results) is used directly. Conventional experimental and other records used in food engineering are not suitable for an efficient uncertainty reasoning. A revitalization of these valuable records is a retrospective application of knowledge engineering algorithms. The final goal of the revitalization is to insert additional information items (e.g. expert guess) to increase the reasoning power. One revitalization technique (fractal analysis) is presented. Two realistic case studies (meat chilling and malt modification) are given in full detail.

The qualitative and semiqualitative analysis of environmental problems

Abstract The analysis of many biochemical engineering problems in environmental modelling is based upon the development and solution of sets of differential equations. A complete analytical solution of such a model requires that every numerical constant in this set of equations is precisely known. This paper describes the use of methods from artificial intelligence which permit the solution of such sets of equations when some constant values are unknown. The use of the methods are illustrated with the solution of a set of equations representing one model of an anaerobic fermentor, and a computer program that implements the methods is described.

A rough set approach to reasoning under uncertainty

Abstract Reasoning with uncertain information is a problem of key importance when dealing with information about the real world. Obtaining the precise numbers required by many uncertainty handling formalisms can be a problem. The theory of rough sets makes it possible to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. This paper presents an introduction to various forms of reasoning under uncertainty that are based on rough sets. In particular, a number of sets of numerical and symbolic truth values which may be used to augment propositional logic are developed, and a semantics for these values is provided based upon the notion of possible worlds. Methods of combining the truth values are developed so that they may be propagated when augmented logic formulae are combined, and their use is demonstrated in theorem proving.

Qualitative, semiqualitative and interval algebras, and their application to engineering problems

Abstract This paper proposes the use of semiqualitative modelling for reasoning about the behaviour of complex physical systems. Semiqualitative modelling is a generalization of qualitative modelling which refines the set of intervals that values may be expressed in. Semiqualitative algebras are introduced, their most important features discussed, and related to qualitative algebras. The advantages that semiqualitative modelling offers over qualitative modelling are demonstrated by the solution of an example from the field of biotechnology. Finally, interval algebras are introduced as a generalization of semiqualitative algebras, and it is proved that it is possible to switch between different interval algebras in the course of computation in order to preserve the greatest possible degree of precision.

A semiqualitative approach to reasoning in probabilistic networks

Abstract This paper proposes the use of semiqualitative modeling for reasoning in probabilistic networks. Semiqualitative modeling is a generalization of qualitative modeling that refines the set of intervals in which values may be expressed. The advantage of semiqualitative modeling of probabilistic reasoning over more traditional methods is that a semiqualitative model can cope with incomplete and imprecise information that would prevent a more traditional model from functioning. The semi-qualitative analysis of a well-known example from the literature is presented, and conclusions about the general use of semiqualitative modeling in reasoning under uncertainty is discussed.

A fuzzy upgrading of integrated vague managerial and engineering knowledge

In reality it is not possible to separate managerial and engineering knowledge without a considerable information loss. Realistic integrated conventional managerial/engineering files are rather vague and very sparse. They are not suitable for direct applications of fuzzy reasoning algorithms. A fuzzy upgrading is a retrospective application of the knowledge of engineering methods and integration of large set of specific and rather isolated detailed information items. Any upgrading is highly subjective and ad hoc in nature. The upgrading objectivity can be increased by a set of upgrading algorithms. Two of them (discriminative power evaluation and fractal analysis) are described. The fractal analysis gives a trade-off between general and specific knowledge items and is presented separately as Appendix. A case study (an upgrading of sugar-cane-plant knowledge base) is presented.

A robust logic for rule-based reasoning under uncertainty

A symbolically quantified logic is presented for reasoning under uncertainty that is based upon the concept of rough sets. This mathematical model provides a simple yet sound basis for a robust reasoning system. A rule of inference analogous to modus ponens is described, and it is shown how it might be used by a reasoning system to determine the most likely outcome under conditions of uncertain knowledge. An analysis of the robustness of the logic in rule-based reasoning is also presented.<<ETX>>

An Extended Logic Language For Representing Belief

The DRUMS project is addressing a variety of symbolic and numerical techniques for reasoning under uncertainty and with incomplete information. This paper discusses work which is directed towards identifying a unifying framework which will enable a variety of uncertainty handling techniques to be integrated in a single programming environment.

The Semiqualitative Analysis of Waste Water Treatment

The solution of environmental problems such as waste water treatment are usually based upon the development of sets of differential equations. A complete analytical solution of the model requires that every numerical constant in this set of equations is precisely known. This paper describes a computer program that implements a method from artificial intelligence which permits the solution of such sets of equations when constant values are unknown, whilst allowing those values that are known to be used. The use of the system is illustrated with the solution of a set of equations describing an anaerobic fermentor.

Bestimmung des Foulingfaktors an Wärmeübertragungsflächen mit Hilfe einer Fuzzy-Wissensbasis

ZusammenfassungDie Bestimmung des Foulingfaktors bei Wärmeübertragern ist objektiv nur bedingt möglich. Aus diesem Grund wurden einige aus der Literatur entnommene Empfehlungen zur Bestimmung des Foulingfaktors mit den Methoden der Fuzzy-Heuristik aufgearbeitet. Durch wissensbasierte Methoden wurden diese primären Erfahrungen als Fuzzy-Sets von einfachen bedingten Aussagen formuliert. Wegen der Vielfalt und Komplexität der Foulingproblematik war es nur möglich, Regeln für den Foulingvorgang durch Sedimentation und den Foulingfaktor für Kohlenwasserstoffgemische aufzustellen. Anhand einer möglichen Version des Fuzzy-Verfahrens werden zwei wirklichkeitsnahe Fragestellungen sowie deren Beantwortung detailliert beschrieben.