Imagine the following scenario: After some investment and several projects, you finally managed to increase the level of instrumentation of the processes of your operation. A number of steps to survey needs, investment estimates, still more estimates of the return of those investments, and much negotiation is needed until the necessary resources are approved. Then several more months pass until the best measurement solutions are implemented and integrated into your automation infrastructure.
With a few clicks you can monitor the consumption of energy and utilities registered in each meter installed in your operation – something once unimaginable, but now within the reach of all process analysts. The positive effect for the management of the operation is immediate – in the words of Arthur C. Clarke, "Any sufficiently advanced technology is indistinguishable from magic."
Various reports, dashboards, and analyses are generated every day, and the great accuracy of measurements makes it possible to revisit previous assumptions, promoting even more discussions, more analysis and more results in a virtuous maturity cycle of energy and utilities management.
But a few more months pass, and the enthusiasm behind this transformation begins to dissipate. You realize that monitoring the energy consumption of processes and operations is only part of the problem (or part of the solution); being able to explain the positive or negative variations in consumption – and find opportunities for improvement – will probably require new capabilities.
In fact, it is not enough to know, for example, how many kWh were registered yesterday by a given meter. Other key questions also need to be answered:
- Which assets are being monitored by that meter?
- What are the characteristics of that stage in the production process?
- How much input was consumed to generate what quantity of what type of product?
- What are the characteristics of the environment (type of work, ambient temperature, for example) and do they affect the performance of the process?
The resolution of the data collected should be compatible with the dynamics of the phenomena observed so that more assertive analyses can be performed.
Deciphering the context of energy consumption
Understanding the context in which data on absolute energy consumption was collected is essential for identifying the causes of variations of energy performance common to industrial production processes as well as the opportunities for gains.
For example, in more abundant instrumentation scenarios, several meters may be connected in a network, forming a measuring hierarchy with redundancies that allow the calculation of balances, the distribution of measurement uncertainties, and the rapid detection of leakages or load losses.
The integration of information about production (typically coming from Manufacturing Execution Systems) adds an important element to the context: the quantity produced, whether in units, tons, or any other physical dimension.
Now, you can also evaluate specific consumption, that is, how much energy was necessary to generate a unit of product. Variations in energy consumption at different times can be analyzed with a single basis for comparison: energy efficiency of the process.
Other information about the quantities produced could also be used, facilitating stratification of consumption levels by dimensions of:
- Product families;
- Production shifts;
- Batches or production runs;
- Process routes, among many others.
Energy efficiency studies of different types of operations then become possible due to the enabling of in-depth analyses of the variables of interest.
Along this same line, the energy performance of a particular process could be evaluated over a period of time, identifying the elusive golden batches (runs or lots that had the best performance observed in that operation), as well as the main causes of the deviation mapped to the process control variables.
Once the operational parameters (referred to as relevant variables) that directly influence the energy behaviour of a piece of equipment or process are understood in depth, you note that it would still be possible to perform comparative analyses between similar equipment or processes, both in the operation you manage and in any other operation you have access to.
This benchmarking process would make it possible to identify processes with superior performance and, consequently, to suggest new opportunities for improving energy performance throughout the operation, observing everything from physical aspects such as installation, type of work, and environmental characteristics, to subjective aspects, such as the level of training of the operation and maintenance teams.
Excited with the possibilities, you see the opportunity to bring information (no longer simply data, but now information) about energy performance to the executive level, checking the factors responsible for the variations in consumption of the entire operation over the long term.
The Pareto distribution of the most relevant variables could be organized in visual analyses (e.g. in waterfall or bridge charts) for the stratification of energy consumption. Furthermore, in addition to highlighting the causes of the variations in consumption, you take advantage of one more possible integration – with the level of Enterprise Resource Planning (ERP) – and convert the variations into financial and environmental losses and gains, highlighting the direct effect of energy on the outcome and on the sustainability of the business, and establishing the strategic positioning of the organization in relation to energy and utilities management.
In this way, all information derived from these analyses can be converted to explicit knowledge for management teams. In fact, understanding the causes of consumption variation is a task that requires effort and investment, but that can bring significant gains, especially when facilitated by specialized tools and teams well-trained in the concepts of of the energy and utilities management system.
The Viridis energy and utilities management platform provides features to directly support initiatives for monitoring, analysis, simulation, and optimization of energy efficiency in industrial organizations. The monitoring functions permit the identification of opportunities for process improvement, stratifying energy consumption in terms of different dimensions, in addition to allowing the quantification of gains in terms of costs and greenhouse gas emissions. Regardless of the level of instrumentation available, the Viridis platform offers many opportunities for research on the main variables that influence energy consumption, either from data collected automatically, collected manually, or calculated. Integration with production management systems and corporate systems allows the capture and analysis of energy performance indicators for assets at any organizational level, taking into account the uncertainties associated with measurements, the balance of measurement networks and the reconciliation of mass and energy balance data. Click here to learn more about our solutions.