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The nal chapter, Applications of Evolving Intelligent Systems to Oil and Gas Industry, is written by Jos Juan Mac Hernandez of CEPSATenerife Oil Re nery and e as University of La Laguna, Tenerife, Spain, and Plamen Angelov of Lancaster University, Lancaster, UK. This chapter reviews the opportunities for application of evolving systems to different process-control problems in oil re neries and in process industry in general. Special attention is given to the evolving model-based industrial (indirect) inferential sensors. The authors introduce an interesting e-Sensor version of the eTS algorithm as an adaptive inferential sensor that is employed for product quality monitoring. They also demonstrate how the Takagi-Sugeno modeling technique can be expanded to the problem of automatic selection of a subset of important input variables that are used to predict estimated process characteristics. Practical aspects of the design and implementation of evolving inferential sensors, including initialization, calibration, prediction, and performance under different operating modes, are also presented. To view supplemental material for this book, including additional software for downloading, please visit http://www.lancaster.ac.uk/staff/angelov/Downloads.htm. In closing, the editors would like to express their gratitude to all contributors and reviewers for making this volume a reality. We hope that this book will be a useful tool and inspiration for better understanding of the philosophy, theoretical foundations, and potential of practical applications of the EIS. The Editors, Lancaster, UK Dearborn, Michigan, USA Auckland, New Zealand February 2010

Although there can be complicated scenarios requiring sophisticated transformations, we will only discuss a few basic rules and techniques to consider when forward generating an LDM written in the RDM from a CDM written in the ERM language. We will consider these rules and techniques for this speci c conceptual modeling language (CML) to be a representative (but far from exhaustive) example. In general, we need to consider how we must transform certain con gurations and certain constructs in order to generate the equivalent semantics in the LDM or at least how to transform the constructs into a set of LDM constructs capable of capturing some of the same semantics. In particular, some AT avors do not translate directly. Similarly, some RT, ET, and IT con gurations require special handling. In general, some situations have multiple generation alternatives, with various bene ts and disadvantages for each. The rules listed in the following sections describe how to map constructs from the ERM into the RDM these rules are grouped by the construct type, not the order in which they need to be applied. In practice, the constructs in the ERM are considered one at a time, and the appropriate rule is applied based on the order in which the constructs are considered. In other words, even though we will consider the rules for ATs rst in this chapter, attempting to transform all ATs as a rst step is not necessary or recommended.

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Inland groundwater discharge zones: natural features Springs, wetlands, ponds, lakes, streams, and rivers can all function as signi cant groundwater discharge zones. The nature and dynamics of these natural inland groundwater discharge features are discussed in detail in 5. In any one landscape it is common for one or more of each class of features to occur in close proximity to (and in hydrological continuity with) another. For instance, springs commonly give rise to the headwaters of streams, and also often ank the banks of streams and rivers over great distances. Other springs can often be seen to feed wetlands, ponds, and lakes, whence surface out ows usually carry water onwards to streams and rivers. Although spring discharges are conspicuous, in many catchments they actually account for a relatively small proportion of total inland groundwater discharge, with the bulk being accounted for by direct groundwater up ow through the bed sediments of surface water bodies (wetlands, ponds, lakes, streams, rivers).

The z test statistic of Equation 25.25 has some favorable properties. It is invariant under monotonic transformation of the variables, it does not require the variables to have a speci c distributional shape, and it is insensitive to outlying values. The weakness of the test is the assumption of complete independence of X and Y, which has limited applicability. Later in this chapter we discuss a variance term that forms the basis for methods with wider applicability.

= lim h(Xn |Xn 1 , . . . , X1 ).

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This is based on Carson (Ref. 4), where the premise is noise just perceptible by a population of TV viewers, with an NTSC 4.2-MHz TV signal. Adding noise weighting improvement (6.8 dB), we nd:3 S/ N C/ N + 2.7 dB. (15.5)

54 O Dwyer JJ Theory of Conduction in Dielectrics, Journal of Applied Physics 1969;39: 3887 90 55 DiMaria DJ, Arnold D, Cartier E Impact ionization and positive charge formation in silicon dioxide lms on silicon Applied Physics Letters 1992;60:2118 20 56 Fischetti M, DiMaria DJ, Brorson SD, Theis TN, Kirtley JR Theory of high eld electron transport in silicon dioxide Physical Review B 1985;31:8124 42 57 Schuegraf KF, Hu C Hole injection oxide breakdown model for very low voltage lifetimeb extrapolation Proceedings of the International Reliability Physics Symposium (IRPS), 1993 p 7 12 58 Klein N, Solomon P Current runaway in insulators affected by impact ionization and recombination Journal of Applied Physics 1976;47:4364 72 59 Nissan-Cohen Y, Shappir J, Frohman-Bentchkowsky D High yield and current induced positive charge in thermal SiO2 layers Journal of Applied Physics 1985;57:2830 9 60 Chen IC, Holland S, Hu C.

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