Hydrothermal modeling for optimum temperature control : an estimation-theoretic approach
Author(s)Schrader, Bradley Philip; Moore, Stephen Fesler
A short-term temperature forecasting (STF) system is proposed to predict and control plant intake and discharge temperatures at Salem Harbor Electric Generating Station. It is desired to minimize receiving-water (i.e., intake-water) temperatures during peak power demand periods, in order to minimize the cost of complying with the maximum discharge water temperature limit. This study addresses the hydrothermal modeling requirements of an STF system. An important element of an STF system is a predictive model of plant intake water temperatures. For application to Salem Harbor Station, strict model performance criteria exist, defining a model development problem: Develop a simple model to predict plant intake water temperatures 24 hours ahead, predicting daily peak intake temperatures within 10F on 90% of the days, and using only existing measurements. An estimation-theoretic approach to model development is used, which quantifies and minimizes the uncertainties in the model. The approach employs optimal filtering and full-information maximum- likelihood (FIML) estimation to obtain optimum parameter estimates. A two-basin, two-layer hydrothermal model of Salem Harbor is developed. The model computes hourly intake temperatures, incorporating tidal flushing, stratification, surface heat exchange, and wind advection of the plume. Twenty-eight model parameters and five noise statistics are estimated from intake-temperature data. Preliminary best-fit parameter values are obtained subjectively, followed by FIML parameter estimation using a data base of 96 hourly measurements (7/29 - 8/2/74). The model is tested for 106 days (5/17- 9/20/74) and various performance measures are computed, including sum- of-squares of measurement residuals (S), whiteness (P), percent of daily peak temperature predictions within 10F of actual (T), and others. Visual inspection of 24-hour intake temperature predictions shows that the two-basin, two-layer model performs qualitatively well. However, the model fails statistical tests on S and P, indicating structural weaknesses. FIML estimation yields physically unrealistic values for certain parameters, probably compensating for inadequate model structure. Despite structural flaws in the two-basin, two-layer model, FIML estimation yields parameters with consistently better performance than the preliminary estimates (by a small amount). It is concluded that the two-basin, two-layer model is presently unsuitable for STF use, largely due to structural weaknesses. Possible corrections are suggested; however, a statistical model of hourly temperatures appears to offer greater potential accuracy than physically-derived models. FIML parameter estimation is shown to be useful for water quality model development on a real system, particularly after subjective model development has been exhausted.
Originally presented as part of the first author's thesis, (Environmental Engineer) in the M.I.T. Dept. of Civil Engineering
MIT Energy Lab
Thermal pollution of rivers, lakes -- Mathematical models, Electric power-plants in Salem -- Mass
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