Material balance reconciliation

Analytical solution Waste heat recovery (H.l) Pipe diameter (13.1) Material balance reconciliation (16.4)... 

EXAMPLE 16.4 STEADY-STATE MATERIAL BALANCE RECONCILIATION... 

The first case study, using simple material balances within a section of an olefin plant, fully exploited the theoretical results discussed for linear systems, namely, variable classification, system decomposition, data reconciliation, and gross error detection and identification. 

While some reconciliation models only have material balance relationships, more meaningful reconciliation results are obtained with models that include material balances, heat balances, equilibrium constraints (both in the separation and reaction domains), rate relationships (heat transfer, mass transfer, momentum transfer, and kinetics), as well as equipment-specific relationships. In other words, one should include more than just material balance constraints when reconciling a model. Heat balance, kinetics, transport relationships—if needed for the... 

Reconciliation of inaccurate process measurements is an important problem in process control that can be solved using optimization techniques. The flow rates of streams B and C have been measured three times during the current shift (shown in Fig. El9.7). Some errors in the measurement devices exist. Assuming steady-state operation = constant), And the optimal value of wa (flow rate in kg/h) that minimizes the sum of the squares of the errors for the material balance, + wc =... 

Data reconciliation is the process of adjusting or reconciling the process measurements to obtain more accurate estimates of flowrates, temperatures, compositions, etc., that are consistent with material and energy balances. 

In the second example, that of an industrial pyrolysis reactor, simplified material and energy balances were used to analyze the performance of the process. In this example, linear and nonlinear reconciliation techniques were used. A strategy for joint parameter estimation and data reconciliation was implemented for the evaluation of the overall heat transfer coefficient. The usefulness of sequential processing of the information for identifying inconsistencies in the operation of the furnace was further demonstrated. 

Another important optimization problem that is solved frequently is data reconciliation. All measurements have some degree of uncertainty, and the measurements need to be reconciled so that the entire set of measurements is consistent with plant material and energy balances. This is particularly useful for monitoring inventories and for improving model predictions used in MPC. 

In Computer Aided Operation we can mention the real time monitoring of material and energy balance, managed nowadays by means of data reconciliation programs. The plant operation can be adapted and optimised in real time by means of computerised tools based on dynamic flowsheeting. Other advanced applications are simulators for safety studies and operator training. 

The objective of this investigation was, therefore, (i) to assimilate flie rqipropriate plant data from various sources, (ii) to determine and estimate their accuracy, (iii) to calculate closed mass balances for defined intervals of time, and (iv) to develop quantitative model to predict the production of ZIC and its composition based on the feed materials and their analysis. In the course of the investigation, the mass balance model was developed first (Model 1) with the aid of data reconciliation. Then, the prediction model (Model 2) was calculated based on the results of Model 1 incorporating a multi-parametrical regression analysis, hi this paper, the capabilities of the combination of these two modem computer-aided tools will be demonstrated as applied to the Ruhr-Zink flowsheet. With these methods, a plant simulator can be updated in a relatively short time that incorporates the latest changes in the flowsheet. 

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