The profitable path to cutting carbon

Founded on sophisticated production intelligence software, a new breed of information-enabled energy management systems is taking on the carbon footprint challenge - while simultaneously improving production performance and the bottom line.

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. Although most governments recognise that carbon emissions must be reduced, the proposed targets and level of commitment vary considerably. Unfortunately, political agendas and capitalist considerations have a tendency to obstruct the formation of a legal treaty that has purely environmental interests at heart.

Nevertheless, governments and related agencies are passing legislation that deals with emissions reduction and demand industries to be accountable for their energy consumption. Irrespective of whether the targets are high enough, the penalties severe enough or the underlying motives good enough, industry is being marshalled down the path towards more sustainable practices.

The Australian Government’s Carbon Pollution Reduction Scheme is set to take effect in 2011, pending parliamentary approval. As a precursor to this, an Australian national framework for the reporting of energy emissions was introduced in 2007. The National Greenhouse and Energy Reporting (NGER) Act 2007 requires all industries with emissions above a certain threshold to report annual greenhouse gas emissions, energy production and consumption. 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.

Meanwhile, in New Zealand the government passed amendments to its proposed emissions trading scheme in November 2009. These amendments will see the scheme implemented in a limited number of industry sectors, meaning that most New Zealand businesses will therefore not participate. However, businesses can choose to monitor their emissions and buy carbon credits through brokers to offset their emissions.

Laying hands on the data

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 analog meters and relying on data provided by utilities companies - are largely imprecise, prone to human error and lacking in resolution or granularity. And, while they may suffice for the administration purposes of NGER, such manual reporting methods will become increasingly draining on resources as the reporting burden increases and energy-reduction strategies are enforced.

The upshot is that companies will need to explore automatic energy monitoring options if they are both to obtain the full spectrum of information that is available, and leverage this to make improvements. Information-enabled control systems that capture, analyse, store and share energy data with other control disciplines across the enterprise will be a critical component, and assumed present in the industrial companies of the future.

Win-win scenario

Installation of appropriate monitoring equipment throughout a plant is the first step. Ideally, networked digital monitoring equipment that can feed data into storage databases should be deployed at all incoming energy streams (water, air, gas, electricity and steam) and all significant equipment. The ability to access and share the information is essential - once the data is available it can be used in various different ways to help optimise both energy consumption and plant operation for a win-win scenario.

Sophisticated, web-enabled software tools play a crucial role here. Even fundamental software packages will permit analysis of the collected data to provide reports on load profiling, power quality and demand, cost allocation, billing analysis and alarming, to name a few. Such information can help identify peak power demands to facilitate rescheduling; highlight leaks, inefficiencies and production problems; and allocate costs based on production area sub-metering, rather than on a simple production footprint basis.

These relatively simple measures pave the way for greater production efficiency, thereby helping reduce energy demands and improving production throughput simultaneously.

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.

Predictive modelling

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.

Advanced software tools can help energy management decision-making by modelling the costs associated with resources and plant assets. Here, an economic cost profile of plant steam demand is modelled for five key plant assets to determine the relative cost of generating the resource internally or procuring from an external supplier.

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.

The power of this type of analysis was demonstrated recently at a food production facility, which was under the impression its new variable-speed drive (VSD) was cutting energy consumption and associated costs. However, an analysis of the system revealed that someone had changed the start-up parameters in the VSD, causing it to act like a direct-on-line starter instead of offering soft-starting capabilities - thus negating the cost-saving purpose of the VSD. The key here was being able to isolate the problem area quickly and easily, and subsequently effecting rapid change.

Power of information

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 re-use of waste products in the reverse supply chain.

There are several conventional drivers for implementing information-enabled energy management systems: legislation such as NGER or the pending Carbon Pollution Reduction Scheme, where penalties will apply if energy-consumption is not reduced; commercial considerations, where energy saved directly results in reduced costs; or simply an environmental conscience.

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, Rockwell Automation Industry Solutions Manager - South Pacific: Information-enabled energy monitoring solutions help obtain information and leverage it to make improvements.