Although hyperbaric oxygen (HBO) is as effective as cephalothin against osteomyelitis due to Staphylococcus aureus in the rabbit, the effect is not by directing killing. To investigate the mechanism, argon washouts (perfusion) and oxygen tensions were measured by intramedullary probes placed in the metaphyses of infected and uninfected tibias. In vitro phagocytic killing activity for S. aureus was determined at oxygen tensions found in these bones under ambient and HBO conditions. Mean tibial oxygen tensions (mm Hg) under ambient conditions were 21 (infected) and 45 (uninfected); under HBO conditions, 104 (infected) and 321 (uninfected). Perfusion was decreased in osteomyelitic bone and was not acutely increased by HBO in either normal or infected bone. Phagocytic killing of S. aureus was markedly decreased at 23 mm Hg of O2, significantly improved at 45 and 109 mm Hg, and most effective at 150 mm Hg. Thus, in osteomyelitic bone, HBO increased intramedullary oxygen to tensions consistent with normal phagocytic function.

No abstract

This paper describes a new application of modern estimation theory to nonlinear chemical processes. A particularly convenient form of the extended Kalman esti-mator is presented and its applications are discussed. T h e method described may be used to compute nonmeasureable process states and system parameters in real time with an on-line process control computer. An application of the extended Kalman filter to a six-dimensional nonlinear well-stirred reactor is discussed in detail. T h e results clearly indicate the feasibility of on-line application of this technique. In some cases these computations could eliminate the requirement for an on-I ine ona ly s i s . In modern control applications, a description of the physical processes i s required. Often in the chemical processing industries, lack of an appropriate mathematical model of the process prevents effective control. Some of the difficulties associated with model building and parameter identification in multivariable systems may be overcome through the use of extended Kalrnan estimation (2 ). Since the complete nonlinear structure of the model i s retained in this technique and a measure of the accuracy of the models i s directly obtainable, estimation theory lends itself readily to development of models.The extended Kalman estimation technique i s well established and has been applied to many defense and aerospace systems ( 2 to 4 ) . Recent applications in the electric power industry (5) and in supercritical shipboard boilers (6) and tubular reactors (7) indicate a trend toward process industry applications.Section I presents a summary of the linear and extended Kalman equations. Section I1 develops sets of normalized estimation and identification equations for use with the extended Kalman filter. A characteristic set of reactor parameters and typical instrumentation system accuracy i s selected. Reactor responses and simulated observations are also computed. The application and results are presented in Section 111. A summary which includes the conclusions and recommendations for future stndy is presented in Section IV. I. THE EXTENDED KALMAN FILTER Linear Kalman FilterThe problem of finite-time optimal filtering and prediction for linear systems with nonstationary random noise was first solved by Kalman (8). Additional results for application of this technique to discrete time systems ((?,lo) led to easy implementation on a digital computer.The discrete Kalman filter equations specify an optimal estimate of the state of a linear, time-varying, dynamic system observed sequentially in the presence of additive white Gaussian noise. The estimate obtained at each time i s the maximum likelihood estimate conditioned on all observations up to that time. This i s equivalent to the leastsquares estimate of the state, since disturbance noises are Gaussian. The vector difference equationx k + l = @k x k + r k W k describes such a system where k denotes a particular in-

No abstract

No abstract