Data acquisition

Keeping an AI on your plant

Empiricai’s AI-based Industrial Analytics platform empowers industries to improve yield and reliability while reducing costs. Instrumentation Monthly recently caught up with Emipircai’s CEO Salman Chaudhary to talk about artificial intelligence (AI) and the value it brings to plant management

What does Empiricai’s Industrial Analytics do?

Plant data is being produced from tens of thousands of sensors in an industrial plant every second. This data helps industrial plant operators and engineers to evaluate whether the plant is running optimally, to troubleshoot when there is a problem that relates to quality or output, or to track the consumption of resources or energy. And, of course, plant operators and engineers are constantly tweaking all of the parameters they have under their control to maximise the yield of the product they are producing, to maximise the consistency of the product, and to do it at the lowest cost, consuming the least amount of resources. Doing this requires analysis of the data to look at patterns and then, based on the direction of these data values, evaluating if the plant is operating efficiently or if corrective action if required. However, industrial data analysis is a research intensive process which currently has a long life cycle due to inefficiencies in many steps of this process. These limitations can lead to delays in obtaining valuable insights that can result in production losses and increased cost. Our Industrial Analytics product is a software

platform that equips users with advanced self- service analytics in an industrial plant setting. It allows process engineers and performance engineers to rapidly investigate and share insights from plant data and enables them to do it on their own without the need of an IT specialist or a data scientist. The platform contains features that will allow you to look at and manipulate data correlations, anomalies and trends to keep the plant running without disruption or to solve a specific problem you may be troubleshooting.

What are the key benefits of Industrial Analytics?

Industrial Analytics improves productivity for plant engineers and process engineers on


everything that they need to do ranging from completing daily tasks through to troubleshooting emerging problems. The platform helps improve plant performance, increase machinery reliability, reduce energy consumption, reduce environmental impact, and reduce raw material wastage. These are all things that plant engineers and operators aim to do and we make it easier for them to do so by making specific and targeted KPIs that can be measured, monitored and refined. We have a base module that is the Industrial

Analytics platform and on top of that we enable customers to add on any AI machine learning or physics-based models that help them to solve a particular problem or specific use case. So, for example, if the customer already has a model that helps them optimise the net heat exchange rate within a particular part of the plant, they can integrate that seamlessly within our analytics platform and leverage the dashboards and the user interface of our product to be able to utilise that model. Customers can do that for a number of different models, whether they are models they have created themselves, third party models, or a custom Empiricai model; they can all be integrated into the same product to help solve problems of efficiency, quality and wastage.

Can Industrial Analytics be used with any equipment?

Industrial Analytics integrates with the data that comes into a distributed control system (DCS) or a historian from sensors throughout the plant. As long as there is data that is available, that data is then leveraged by our product. It doesn’t matter what machinery or equipment there is, as long as there are sensors that are providing data, Industrial Analytics can operate. It is completely agnostic around specific types of machinery and equipment in terms of the manufacturer but we do rely upon data being supplied from some source. So pure analogue systems that may not have any sensors would not be suitable for the platform.

How easy is it to set up?

It is designed to be a self-service product so it is very easy to set up. All Industrial Analytics needs is to connect to a source of data - often that is a historian system or data that is derived from a control system that is on site. So the set up is very easy for the data to be imported into our product and that can be done on a real time basis, a batch basis or an overnight/ once-a-day basis. There are two versions of the product - the

first is a complete SaaS product based in the Cloud. It is simply installed, connected to the data sources, the data is populated and then you are ready to start doing some analysis. We also provide an on-premises version of the product - certain industries or industries in certain countries may have a specific requirement to be on-premises so we have a product for that and, again, that requires a very simple install of the software. There is some configuration that needs to

be done in terms of setting up users and tagging some of the data that may not be tagged but that is a process that we can take our customers through. A challenge that often appears in the setup

stage revolves around the quality of all the data as well as the completeness of the data. We often find that there are sensors that are faulty or that are not providing data, or that the data is getting corrupted somehow and it is not being caught by the control systems or the historians. Sometimes the sources need to be replaced or fixed as part of the implementation process. Of course, we’ll still take on that data but the data may not be as useful if it is not complete.

How long is the data stored for?

There are no limits from our side on how much customers can store and how far back they can go in their history to analyse problems or to do

November 2020 Instrumentation Monthly

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