Real-time optimization data processing

Some recent applications have benefited from advances in computing and computational techniques. Steady-state simulation is being used off-line for process analysis, design, and retrofit process simulators can model flow sheets with up to about a million equations by employing nested procedures. Other applications have resulted in great economic benefits these include on-line real-time optimization models for data reconciliation and parameter estimation followed by optimal adjustment of operating conditions. Models of up to 500,000 variables have been used on a refinery-wide basis. 

The use of online data together with steady-state models, as in Real Time Optimization applications, requires the identification of steady-state regimes in a process and the detection of the presence of gross errors. In this paper a method is proposed which makes use of polynomial interpolation on time windows. The method is simple because the parameters in which it is based are easy to tune as they are rather intuitive. In order to assess the performance of the method, a comparison based on Monte-Carlo simulations was performed, comparing the proposed method to three methods extracted from literature, for different noise to signal ratios and autocorrelations. 

Petroleum shortage is an important issue. Improving the efficiency of refineries by optimising their operation is one of the measures that must be implemented. In order to do so using computational tools available, like Real Time Optimization (TTTO), it is mandatory to use data obtained in steady-state operation. This justifies the need for steady-state detection procedures, because the adaptation of process models to data obtained exclusively in steady state operation leads to better solutions (Bhat Saraf, 2004). 

There are several advantages of the use of HPLC for process monitoring. First, HPLC provides both qualitative and quantitative information about a process. At the research or pilot reactor stage of development, real time monitoring increases research efficiency and provides the data for process optimization. Second, because HPLC permits continuous real-time monitoring of reactors or other process components, process upsets that might go... 

Real time. The clock cycle for the collection and transfer of process data and the optimization calculations is the same. 

The main objective for an intelligent system in electrolyser operations is to gather and process valuable information for a greater control and efficiency. To achieve the aforementioned objective, two key functions have to be performed properly. Firstly, accurate and precise real-time data need to be obtained and secondly, the system should be able to process and interpret these data based on fundamental and acquired industrial electrochemical knowledge. In the case of R2 s EMOS , this second key element refers directly to its capability to use embedded human expertise to find optimal operating solutions and to detect and correctly identify equipment degradation or other anomalies. 

The late 1960s saw the appearance of dedicated laboratory mini-computers and the third generation of instrument systems (Fig. 8.4). The computers were interfaced to existing instruments and were used primarily to log and process data. In some cases, simple instructions could be sent to the instrument by programs resident in the minicomputer. It was also possible for the computer to optimize instrument conditions in real time by monitoring output data. 

Developing such as model is a preliminary and necessary stage in achieving real time plant optimization which involves treating data reconciliation and rigorous simulation simultaneously by means of optimization techniques, whose objective is to maximize process profitability (Perunicic et.al.,2007). 

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