The heat and material balance is a fundamental process for petrochemical plants and while it may look easy, it can hide many complications in terms of tools, information technology, and in understanding the process itself. It is a question of getting daily measurements of flow rates and stock, using these to feed a plant model that can predict unmeasured quantities and minimize systematic measurement errors overall, thereby determining the best solution.
Accurate data and accurate plant representation are essential if the heat and material balance itself is to be accurate.
The benefits of using an automatic system for material balance become clear in terms of yield and production (with an estimated improvement of 3-5% for these KPIs), reduced energy consumption, the ability to identify material loss, and in reducing the time (earlier availability) and effort necessary for producing the balance itself, freeing up resources and adding greater value.
A typical heat and material balance solution is essentially a calculation engine that gathers data from a range of sources: field values, material handling data, financial data and laboratory data.
This information is progressively reworked so as to determine plant operating arrangements, flow rate compensation, and converting flow rates into consistent units of measurement. heat and material balance is calculated by running data and plant model through the reconciliation engine. Finally, results are made usable by filtering them through company business intelligence and reporting systems.
How to achieve a heat and material balance
Techedge has refined a step-by-step method to create effective heat and material balances.
- Defining the architecture and software selection
- Defining the plant model
- Identifying the available data and the validation logic
- Fine-tuning and continuous model improvement
1. Software selection
There is a range of different commercial software available on the market that has been developed for material, energy and/or exergy balances. The choice of software should be based on the type of production (petrochemical, fine chemicals, pharmaceuticals), the size of the plant, and any specific needs that may arise and could be managed by balance software.
These products are then usually integrated with the rest of the business’ software - e.g. with stock management software - and customized (perhaps on the reporting level, for example).
2. Defining the plant model
The plant modelling affects input and output flows, meter positioning, meter accuracy, and losses associated with the equipment. This work must consist of a joint effort by plant managers, as the role of in-depth knowledge and historical memory are key factors.
At times, the available measurements aren’t sufficient and so must be integrated with estimated amounts so that logic application may be automated as much as possible.
For example, let’s take a blender which, depending on the work required, can send a semi-finished product to two flash columns at the same time. The physical meter M is at the blender output, before the switch that directs material to columns A or B. To model the balance correctly, two virtual meters must be introduced, 1 and 2, which obtain the measurement of M by observing the state of columns A and B, respectively. Therefore, when column A is turned off, 1 will measure a null value, and the balance will correctly direct the entire measurement of M to 2.
3. Identifying data, measurements and logic
Choosing data and measurements to be included in the balance is the first step toward an accurate analysis.
In order to correctly allocate flows and measurements, the following must be taken into account: set up and condition of the equipment, meter positioning, meter accuracy.
Meter accuracy is particularly important in order to carry out a reconciliation correctly: too high a level of accuracy will lead to non-converging solutions, which leave various deficits in the model and means it cannot be balanced. On the other hand, when accuracy is too low, the risk is that the model will balance, however not as expected: a flow might appear to be twice its real value after reconciliation.
Therefore, every tool should be allocated its own accuracy class, starting with the datasheets but integrating system knowledge and people’s memory as regards the reliability of each meter over time as well. All this information should be wired into the system, as doing so will make it a shared corporate asset.
4. Fine-tuning and continuous improvement
Fine-tuning, the final part of producing a heat and material balance, is the most important.
Fine-tuning makes it possible to assess balance results over increasingly extended periods, starting with the existing data and the defined model, and therefore allowing for different types of adjustments to be identified and carried out: changes to the model due to inaccurate representation of the plant, flange calibration procedures as a result of gross error analysis, refinement of measurement estimates, etc.
Even if fine-tuning has a time limit, at the end of which the heat and material balance may be considered valid, it is essential to maintain a process of continuous improvement over the years by analyzing the gross errors that come up. Analyzing these makes it possible to have a model that is always accurate and representative of the process, and to identify problems with tools and loss of matter rapidly.
Techedge has gained experience in the design, choice and implementation of software for balance systems, whether by collaborating with suppliers to optimize some of the native features of their software or helping clients configure and adapt the tool to their specific needs. If you want to discover the benefits of applying balance systems to your business, our experts are available for studies and evaluation of the solutions best suited to your needs.
Other articles of this series on Digital Advisory:
- People at the center: the key to a successful transformation
- The role of Technology Onboarding in innovation projects
- The pros and cons of remote Design Thinking
- Checking and monitoring devices remotely: IoT or Historian?
- Overall Equipment Effectiveness (OEE): the KPI for improving production processes
- Digital Twins, a pillar of digital transformation
- Heat and material balances: monitoring yield and consumption, identifying losses