IoT Enabled Energy conservation: How an IoT enabled solution is enabling electricity savings for factories

Energy is the lifeblood of any factory. Whether discrete or process based, rising energy prices & the stringent legislative reforms have made every manufacturer place energy efficiency & conservation among its top priorities. Energy conservation results in not just cost savings, but also increased productivity, easier compliance and more efficient operations.

Many experts recommend real time monitoring systems touted as IoT as the ultimate energy saviours. The crux of the matter is that real time monitoring might lead to monitoring of energy consumption, but not energy conservation directly. Reducing the number of shifts, decreasing the number of hours of functioning is another way to affect energy savings. But these are attributed, towards the goal of increasing the productivity/OEE of the facility rather than a pure energy conservation goal.

Here is how IoT can enable direct energy savings for the smart factory of today. Let us take an example of electrical energy. Usually, the billing of electrical energy of any plant globally consists of two components: Demand charge and Runtime/consumption related charges. Demand load is usually the peak load provided by the electricity service providers from the power grid. This usually has a hard and fast limit, on crossing which, the user is heavily penalised (around 20 times the usual rates). To avoid this, there are usually two options: either you reduce the total load required by the machinery or ensure that the threshold limit is never reached.

The major source of electricity usage in the plant are the electrical motors, which are responsible for the larger chunk of the power consumption. A motor is considered under loaded when it is in the range where efficiency drops significantly with the decreasing load. Most electric motors are designed to run at 50% to 100% of rated load. Maximum efficiency is usually near 75% of rated load. Below the 50% rated load, the efficiency tends to lower dramatically. In most cases, the operating motors are usually underloaded, working at most 40% of their capacity. These motors that are significantly oversized and underloaded result in low efficiency and overheating, causing huge spikes in energy consumption.

Oversized motors have a higher initial cost and are very expensive to repair & maintain. Undersized motors, on the other hand, have poor performance and suffer from higher losses compared to properly sized electric motors. It forces the motors to fail much sooner. Improperly sized electric motors are less efficient and costlier to operate. In the pre-IoT era, the motor load test was a lengthy and cumbersome affair. By using slip tests & electrical tests with a digital stroboscope, the engineers had to spend hours with the equipment to obtain samples. Even then, the data collected was only a sample, and not real time. With IoT in place, the analysis can be then carried out on real time data from the motor, making the analysis quick, painless & more accurate.

With the right IoT platform, you can recommend the proper sizing needed for motors, leading to not jus significant costs on investment, but also optimizing operational efficiency. To ensure the threshold limit of the motor is never reached, so that it does not reduce operational life, bearing failures and mechanical failures, real time conditional monitoring powered by IoT is the optimal solution. The Monitoring system, powered by IoT, gives early warnings of Electric motor vibration/temperature problems. Condition monitoring saves time from unplanned production outages and the unnecessary stress of carrying out urgent repairs.

At Altizon, thus, we cover every aspect of implementation in the initial phase of deployment itself. We believe that IoT can help in solving a lot of issues that are core to hindering the plant from performing to its optimal capacity & efficiency. To this effect, we apply Datonis, our flagship IoT platform in innovative ways at the shop floor to ensure that all the resources used are used optimally, paving the way for a futuristic Industry 4.0 scenario.