w INDUSTRY FOCUS Food & Beverage


Successfully managing data in the food and beverage sector


As IIoT grows, food businesses have growing volumes of data available about all aspects of machine, system and component performance. George Walker, Managing Director of Novotek UK and Ireland, explains how food and beverage manufacturers can keep HMIs streamlined and effective yet harness all that valuable data


“W


e live in a world where there is more and more information,


and less and less meaning,” wrote French philosopher and sociologist Jean Baudrillard in his seminal work “Simulacra and Simulation”. This is undeniably close to the industrial space, too; with such an abundance of information and data, it becomes easier to lose sight of meaningful insights.


Some 2.5 million terabytes (TB) of data is generated every day globally – from e-mails and messages to website analytical data and system performance information. A high percentage of this is from machine- to-machine data sharing, from systems such as supervisory control and data acquisition (SCADA) and the IoT. According to IDC research from 2019, most industries produced between 0.5 and 2TB of data each day. In its “Introduction to data management” whitepaper Novotek found that the pharmaceutical, semiconductor and automotive manufacturing were generating 1.27, 1.73 and 1.9TB each day, respectively. The food and beverage sector produces slightly less data daily, a somewhat below- the-average 0.85TB, expected to rise to 1.53TB by 2025. To put it into context, 0.85TB is equivalent to approximately 63 years’ worth of 4K resolution video streaming each year. In years to come, this will grow further, to rival the data


32 November 2021 | Automation


volumes of the pharmaceutical sector, due to the growing regulatory, traceability and reporting requirements.


So, it’s no surprise that data overwhelm


is a growing problem for food engineers and plant managers; handling this volume of data requires physical and practical considerations.


The hardware/software duo Hardware and software deployments are the most intuitive considerations when facing the prospect of gigabytes of data being generated every minute. Although not every system will have to bear the brunt of 0.85TB fl owing through it each day, engineers must ensure that the right tools are in place to effi ciently and reliably collect, process, share, store and analyse this data. There are diff erent hardware considerations, depending on the specifi c operating environments. An industrial PC needed to collect and present data from an industrial oven in a food processing plant will need to be far more resistant to high temperatures than an IPC in, say, dairy processing, which would need to be ingress-protected and better performing at lower temperatures.


Mechanical considerations aside, it’s the computing performance of hardware that directly relates to its ability to handle data. Data acquisition and short-term storage fi eld applications can use hardware with multi-core, reasonable clock-speed


processors to handle large data sets and process it effi ciently. If real-time processing and transmission is vital, faster clock speeds are necessary. For hardware that will be tasked with multiple functions simultaneously and connected to devices locally and further afi eld, food engineers may need to use several units to ensure reliable performance. Alternatively, a multi- core controller with good single-core performance, such as Emerson’s RX3i CPL410 automation controller, can split processes between several cores to ensure consistent performance with minimal risk. Having a single unit in place makes managing inner and outer loops easier. Irrespective of whether additional software systems are employed to perform more comprehensive analysis of data, it’s important for food-plant managers to ensure fi eld-level hardware can perform eff ective data compression and pre- processing. This reduces the size of the data packets shared between networked devices, as well as reducing costs associated with cloud-based software.


Actionable data With the hardware layer capable of handling enormous volumes of data, it then becomes a matter for the software to make that data actionable. For plant-wide data management and analytics platforms, this is typically achieved by establishing several views, to provide the most relevant


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