Access the full text.
Sign up today, get DeepDyve free for 14 days.
P. Whitehead, P. Young (1979)
Water quality in river systems: Monte‐Carlo AnalysisWater Resources Research, 15
H. Tamura (1974)
A Discrete Dynamic Model with Distributed Transport Delays and Its Hierarchical Optimization for Preserving Stream QualityIEEE Trans. Syst. Man Cybern., 4
M. Singh (1975)
River pollution controlInternational Journal of Systems Science, 6
V. Tarassov, H. Perlis, B. Davidson (1969)
Optimization of a Class of River Aeration Problems by the Use of Multivariable Distributed Parameter Control TheoryWater Resources Research, 5
G. Hornberger (1980)
Eutrophication in peel inlet—I. The problem-defining behavior and a mathematical model for the phosphorus scenarioWater Research, 14
P. Young, B. Beck (1974)
The modelling and control of water quality in a river systemAutom., 10
R. Spear (1980)
Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysisWater Research, 14
D. Auslander, R. Spear, G. Young (1982)
A simulation-based approach to the design of control systems with uncertain parametersJournal of Dynamic Systems Measurement and Control-transactions of The Asme, 104
M. Naito, T. Takamatsu, T. Fukuda, H. Tamura (1972)
Optimum Planning of Sewage Treatment Systems for Preserving Stream QualityIFAC Proceedings Volumes, 5
Gourishankar Gourishankar, Raman Raman (1977)
Design of a water quality controller by pole placementIEEE Trans. Syst. Man. Cybern., SMC7
M. Beck (1978)
A Comparative Case Study of Dynamic Models for DO-BOD-ALGAE Interaction in a Freshwater River
G. Hornberger, R. Spear (1981)
Approach to the preliminary analysis of environmental systems
D. Jacobson (1973)
Notes on optimizationIEEE Transactions on Automatic Control, 18
D. Kendrick, H. Rao, C. Wells (1970)
Optimal operation of a system of waste water treatment facilities, 9
Ozunger Ozunger, Perkins Perkins (1979)
A Nash feedback approach to the control of river pollutionIEEE Trans. Systems Man. Cybern., SMC9
A previously developed regionalized sensitivity analysis for exposing critical uncertainties in models of environmental systems is extended to study control of systems for which there is a good deal of uncertainty in the mathematical model used to describe the appropriate physical, chemical, and biological processes. The method is based on a binary classification of Monte Carlo simulation results as being either satisfactory or unsatisfactory in terms of controller performance. Contrasts in parameters associated with the two classes are elucidated by statistical analysis. This allows the selection of a set of control parameters that maximizes the probability of acceptable behavior in the presence of uncertainty in process parameters. The method is applied to the problem of regulating the discharge from a lagoon with the object of preventing DO from falling below a predetermined standard. It was found that for this system the desired behavior of the controlled process can be achieved with a probability of 0.84 with a particularly simple controller design. Nevertheless, the results suggest that even modest levels of uncertainty in the process parameters can have a considerable effect on the controller performance and that additional attention should be devoted to the design of robust controllers for environmental systems.
Water Resources Research – Wiley
Published: Oct 1, 1983
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.