Abstract
Abstract Reliability of each state of process in many chemical process industries largely relies upon water and vitality supplies. In this way, there is great necessity to have an improved and controlled smart energy distribution network (SEDN) in industries. In SEDNs, sensor information related to flow control and optimization serves as a basis for modelling of energy management systems. Therefore, it is important to ensure that sensor data are accurate and precise. However, they are affected by random noise and measurement biases, which compromise the quality of measurements. Data Reconciliation (DR) is one such approach popularly used in industries to reduce the adverse impact of random errors present in pipe flow measurements. In this study, Python-based simulations of weighted least squares (WLS) and principal component analysis (PCA) based DR techniques are implemented on the selected flow streams of SEDN, and reconciled estimates are obtained. The results show that Root Mean Square Error (RMSE) is the best performance metric since it is more sensitive to small changes in the measurement values and the reconciled estimates. Further, it is observed that PCA-DR performs better than WLS-DR in reducing the random error (and thereby achieving greater precision of measured values).
Highlights
In most chemical industries, utilities such as water and energy play an important role
To analyse the performance of principal component analysis (PCA)-data reconciliation (DR) and weighted least squares (WLS)-DR on the process data, flow variables F16-F1-F28-F5-F3-F18F27 have been considered in the order of increasing base magnitude
The PCA-DR, as seen previously, depends on magnitude of variable in i.i.d. data; in the presence of bias, irrespective of base value, the result was the value of amount of gross error present
Summary
Utilities such as water and energy play an important role. The conventional approach to implement DR is the weighted least square data reconciliation (WLS-DR), which is used to get higher precision estimates of process variables from measurements which have noise added. The goal of principal components is to explain the maximum amount of variance with the fewest PCs. Narasimhan & Bhatt ( ) have described an approach for applying PCA-based DR, a recent technique to obtain reconciled estimates. RER is another measure used to evaluate the performance of reconciliation techniques This measure is the ratio of relative errors between raw measurement and reconciled estimates. The selection of the benchmark systems (Valle et al ; Varshith et al ; Jeyanthi & Devanathan ) is based on the number of variables and interacting nodes in the process This would lend credence to the performance evaluation of the techniques included in the study. The mass balance equation at each node is defined by Equation [17], Input flow variable of ith node
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