If December’s ineffectual Copenhagen climate conference proved anything, it was that a great deal of work remains before world powers will make concrete commitments to an international approach to tackle climate change. The Australian government’s Carbon Pollution Reduction Scheme is set to take effect in 2011, pending parliament approval. The key objectives are threefold: to generate an overall picture of Australia’s industrial carbon footprint; to meet the country’s international reporting obligations; and to prepare industry for emissions trading legislation.
All well and good. But this now begs the question of how to obtain all the required data. Moreover, once the data is accumulated, how can it be acted upon to reduce energy consumption while maintaining production levels and profitability?
Manual methods of data collection – such as reading analogue meters and relying on data provided by utilities companies – are largely imprecise, prone to human error and lacking in resolution or granularity. Installation of appropriate monitoring equipment throughout a plant is the first step. Sophisticated, web-enabled software tools play a crucial role here.
More advanced breeds of software that offer superior reporting, analysis and modelling tools deliver even greater functionality. The data can, for example, be used strategically to create an integrated Energy-Supply model of a plant – essentially an evaluation of how energy resources are used. Here, each energy-generating asset is assessed in terms of generating capacity, efficiency curves and operating costs to yield an economic sub-model (or financial profile). This highlights the effectiveness of existing systems and helps ensure that the most effective energy source for each application is used.
As industry responds to demands for greater production responsiveness and ‘make-to-order’ capability, increased focus is being placed on adapting quickly and profitably to changing plant and market conditions. This has led to the rise of predictive modelling of production processes, including energy consumed, to facilitate proactive decisions based on information fed back into the system – essentially a ‘closed loop’ process performance management system.
Predictive modelling forms the foundation of the next generation of production intelligence. By using advanced software tools to compare different scenarios of future performance against an established baseline, companies can make proactive instead of reactive decisions to optimise processes, act on those decisions faster, and improve their planning.
These sophisticated software solutions are becoming known as ‘predictive-enterprise manufacturing intelligence’ (P-EMI) applications, and incorporate financial information from the business system with high-fidelity process models to empower decision-making.
‘Economic Energy Optimisation’ is one application that falls under the P-EMI realm. Companies can leverage the power of predictive modelling to generate sub-models for utilities, emissions, and production, which are then integrated with the financial system to present the optimum solution for a facility’s predicted demand. This results in real-time and realistic energy consumption forecasts, and identifies areas where possible savings in energy could be made – all while improving plant efficiency and lowering production costs.
Underpinning this new breed of production intelligence solutions are systems to handle and streamline delivery of the information. The greatest efficiency will be achieved with a truly information-enabled architecture – a fully integrated platform of software and hardware that captures, consolidates and distributes data throughout the enterprise in a purposeful and service-oriented way. The goal is to improve information access, relevancy and usefulness, as well as maintain and develop the information over time.
The power of information-enabled architecture, moreover, extends beyond even predictive control and production intelligence to embrace a completely holistic plant view of sustainable operations. The most common view of sustainability concerns processes and technologies that consume minimal energy and resources, and create minimal waste; but a broader outlook encompasses workplace safety, product safety and reliability, and the reuse of waste products in the reverse supply chain.
In future, however, the fourth driver – Economic Energy Optimisation of the total plant, leveraging predictive production intelligence tools – is bound to escalate in influence, particularly as companies come to realise the powerful impact that it can have on not only carbon footprint, but also production performance and bottom line.
[Corrie van Rensburg is Rockwell Automation’s industry solutions manager – South Pacific.]