Analysisand Basis

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Analysis of the energy balance is a powerful tool for achieving an overall view of site energy flows, its losses and efficiency improvement opportunities. It enables a specialist or the person in charge of energy management to learn about the system, its flaws, strengths, weakness and sensitivities. With this knowledge it is possible to establish a structured approach to improvements. The analysis itself has two stages:

• analyzing data collection and calculation methods; and

• analyzing results and inferring actions.

The first stage should be structured and systematic and its benefit is to avoid errors and advance in data availability. This is accomplished by checking the consistency of energy consumption variations for each period with operational logs. Energy flows must follow certain trends and ratios with production rates. If the figures don' t show coherence between them, it is time to verify meters, review formulas, assumptions and eventually consider more metering.

The second stage is an investigative and screening phase. Once the previous one has been approved, energy balance can be analyzed over historical results, benchmarking with data from similar plants and units, to determine energy consumption patterns and forecasts. Comparison with the actual consumption opens the way to find out gaps between them. These gaps can be explained either by inefficiencies or better, energy efficiency opportunities or lack of knowledge of the system that turns the wheel of the learning cycle and improves the energy balance. Benchmarking can be accomplished by some different approaches. One is surveying similar processes, tabulating results, and choosing the best performing processes as the iconic best practice standards to which refinery units and processes must strive to meet. Another option is to generate a benchmark from the plant's own past performance, analyzing operational conditions when that point was reached and comparing it with current performance. With this in hand management can set increasing improvement goals, like being 10% better than the previous best, concerning differences of operation at each moment. A third option is building a performance model based on a software and feeding the equipment' s operational data into models that attempt to simulate the potential performance of each equipment and by instance of the whole refinery. This kind of comparison allows better identification of opportunities for energy efficiency. Such a model can also be used to identify which part of the plant is degrading in performance, and loss of energy efficiency can be prevented by operational or maintenance intervention. It allows the effect of changes in operating procedures, or capital projects to be forecast, and the generation of a dynamic energy efficiency target for the site, taking into account operational mode, throughput, seasonality etc. In large refineries, petrochemical and complex plants this approach is nowadays almost indispensable.

It begins to be possible to determine subtleties, like how much energy really is variable with production and the amount that is fixed, that will always have to be spent regardless the operational mode. The limit from manageable and non manageable consumption is traced. Relating these to processes can provide a detailed scope of areas which offer significant benefits and produces a list of priorities, ranking the size and places where there are better options for improvement, from the so-called 'low-hanging fruit' to the 'hard-cracking nut'.

Assuming that data and calculus methodology are available and consistent, the most important requirement for performing this analysis is practical process knowledge in refining processes. Delegating this procedure to seasoned personnel helps the learning cycle and in the meantime allows maximum improvements in energy efficiency. These specialists will be able to trace the plant itinerary for better results encompassing short, medium and long-term operational actions and investment projects, guaranteeing consistency between them. This knowledge can be translated into simulation tools that can be used dynamically to forecast results and help spread the energy efficiency culture, while the refinery develops and grows over the years. This will ensure that future decisions whether operational, maintenance or investments will be more concerned with energy efficiency options.

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