Energy Efficiency Analytics: Factories

WEEK 2: Energy Efficiency in Factories

Abstract

Energy efficiency monitoring and management for a factory has become increasingly important in recent years due to the need to reduce energy costs and carbon emissions. In this research paper, we focus on asset level analytics in real-time and how it differs from most SCADA and BMS systems. We examine the use of VFDs, HVAC, motor control, IIOT sensors, AI-enhanced predictive maintenance, and other tools that are commonly used in energy efficiency management for factories. We also explore the challenges that factories face when implementing energy efficiency measures and provide some best practices for optimizing energy efficiency.

Intention of this article

The intention of this article is to provide CEOs with an overview of the importance of energy efficiency analytics in achieving their organisation's net-zero goals. This article is not intended to be a step-by-step guide, but rather a high-level overview of the key considerations and benefits of implementing energy efficiency analytics.

As a CEO, it is important to understand the role that energy efficiency analytics plays in achieving net-zero goals, as well as the potential benefits and challenges associated with implementing such measures. By understanding the value of energy efficiency analytics, CEOs can make informed decisions and ensure that their organisation is on track to meet its sustainability objectives.

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Introduction

Energy efficiency is a critical issue for factories as it can help reduce energy costs and carbon emissions. According to the International Energy Agency (IEA), the industrial sector consumes around 37% of the world's total energy and is responsible for around 35% of global CO2 emissions. Therefore, it is essential for factories to implement energy efficiency measures to reduce energy consumption, lower costs, and minimise their carbon footprint.

One of the most significant challenges for factories when implementing energy efficiency measures is the lack of real-time visibility about energy consumption. This information is critical to identifying areas of inefficiency and making data-driven decisions to optimise energy consumption. In this paper, we focus on asset level analytics in real-time and how it differs from most SCADA and BMS systems.

Asset Level Analytics

Asset level analytics is a process of monitoring and analysing individual assets in real-time to identify inefficiencies and opportunities for improvement. This process involves collecting data from various sources, including sensors, meters, and other devices, and using advanced analytics to identify patterns and anomalies in the data.

Analytics is different from most SCADA and BMS systems, which focus on monitoring and controlling systems and processes rather than individual assets. SCADA and BMS systems provide high-level information about the performance of systems and processes, but they do not provide granular data about individual assets' performance. Asset level energy analytics, on the other hand, provides detailed information about each asset's energy performance, allowing factories to identify inefficiencies and make data-driven decisions to optimise energy consumption.

Variable Frequency Drives (VFDs)

Variable frequency drives (VFDs) are electronic devices that can control the speed of motors in HVAC systems, pumps, and other equipment. VFDs are commonly used in retrofit energy efficiency management for factories as they can help reduce energy consumption by controlling motor speed.

VFDs work by converting the AC voltage supplied to the motor into DC voltage and then using an inverter to convert the DC voltage back into AC voltage at a frequency that can be varied. By varying the frequency of the AC voltage supplied to the motor, the speed of the motor can be controlled, which can help reduce energy consumption.

Another function of VFD’s I look at is controlling inrush current.?The inrush current of a motor can vary depending on the type and size of the motor, but it is typically several times higher than the normal running current. The magnitude of the inrush current depends on the impedance of the motor windings, as well as the voltage and frequency of the power source.

As a rough estimate, the inrush current of a motor can be anywhere from 500% to 1000% of the normal running current. However, this can vary widely depending on the specific motor and the application it is used in.

VFDs are typically integrated with SCADA or BMS systems to provide real-time information about motor speed and energy consumption. However, the information provided by SCADA and BMS systems is usually at a high level and does not provide granular data about individual assets' performance. Therefore, asset level energy analytics is essential for identifying inefficiencies and opportunities for improvement in VFD-controlled systems.

HVAC

Heating, ventilation and air conditioning (HVAC) systems are responsible for a significant portion of a factory's energy consumption. Therefore, optimising HVAC systems is critical for reducing energy costs and minimising a factory's carbon footprint.

HVAC systems are typically controlled by BMS systems that provide information about the performance of the system as a whole but very few cover energy efficiency at asset level and in real time. However, asset level energy analytics is essential for identifying inefficiencies and opportunities for improvement in individual HVAC components, such as air handlers, chillers and boilers.

By leveraging energy analytics, it's possible to monitor and analyse the performance of individual components of HVAC systems, such as air handlers, chillers, and boilers, in real-time. This allows facility managers to identify inefficiencies and potential failures before they lead to downtime or increased energy consumption.

Moreover, using predictive maintenance techniques, it's possible to detect anomalies in HVAC system performance and schedule maintenance proactively, reducing unplanned downtime and extending the lifespan of equipment. With the integration of advanced analytics, artificial intelligence and machine learning technologies, HVAC systems can be continuously optimised to improve energy efficiency, reduce costs and minimise environmental impact.

IIOT Sensors

The Industrial Internet of Things (IIOT) refers to the use of internet-connected sensors, meters, and other devices to monitor and control industrial processes. IIOT sensors are increasingly being used in energy efficiency management for factories as they can provide real-time information about energy consumption and asset performance.

IIOT sensors can be used to monitor various parameters, such as temperature, pressure, and flow rate, to identify inefficiencies and opportunities for improvement. For example, IIOT sensors can be used to monitor the temperature of equipment to identify when it is operating outside of its optimal range, which can indicate inefficiencies or potential failures.

These sensors can be used to monitor the energy consumption of different processes and equipment within a factory. This can help identify areas where energy usage can be optimized, resulting in cost savings and reduced environmental impact. For example, IIOT sensors can be used to monitor the energy consumption of lighting systems and adjust them according to occupancy levels or natural light conditions.

IIOT sensors can also be used to monitor equipment performance and predict maintenance needs. By analysing data collected by sensors, manufacturers can identify patterns that can indicate potential failures and schedule maintenance activities to prevent equipment downtime.

Overall, IIOT sensors can play a crucial role in improving the efficiency and sustainability of industrial processes. By providing real-time information and insights, IIOT sensors can help manufacturers optimize their energy usage, reduce costs, and increase productivity.

LoRa or Wifi?

LoRa (Long Range) technology has emerged as a popular choice for Industrial Internet of Things (IIoT) devices due to several advantages over traditional WiFi technology. One of the primary advantages of LoRa is its long-range capabilities. While WiFi has a limited range, typically only covering a few hundred meters, LoRa can reach up to several kilometers. This makes it ideal for industrial IoT applications, where devices may be spread out over large areas or located in remote locations. LoRa can provide coverage for a wider range of sensors and devices, allowing for more comprehensive and reliable data collection.

Another advantage of LoRa over WiFi is its low power consumption. WiFi devices require a constant connection to the internet, which can drain battery life quickly. LoRa devices, on the other hand, only need to transmit data periodically, conserving battery power and allowing for longer device lifetimes. This makes LoRa a better choice for IoT applications that require low power consumption, such as those involving remote sensors or battery-powered devices. Additionally, LoRa networks can be designed with a decentralized architecture, allowing for devices to operate without the need for a centralized hub, further reducing power consumption and improving reliability.

Device security is another crucial factor to consider when implementing IoT solutions in industrial settings. LoRa technology offers several features that make it more secure than traditional WiFi. For example, LoRa devices can use encryption to ensure that data is transmitted securely, protecting against potential threats and unauthorized access. Additionally, LoRa networks can be designed with secure key management systems, which allow for the secure exchange of keys between devices and prevent malicious actors from intercepting or tampering with data. Furthermore, LoRa's decentralized architecture can provide an additional layer of security by reducing the number of potential points of failure.

The long-range capabilities, low power consumption, and improved device security of LoRa technology make it an ideal choice for industrial IoT applications. By leveraging these advantages, industrial IoT devices can be designed and implemented to provide reliable, efficient, and secure data collection and transmission, ensuring that critical information is always available when and where it is needed.

AI-Enhanced Predictive Maintenance

Predictive maintenance involves using data analytics to identify potential failures before they occur. This approach can help reduce downtime, maintenance costs, and improve asset reliability.

AI-enhanced predictive maintenance involves using machine learning algorithms to analyse data from sensors, meters, and other devices to predict when maintenance is needed. This approach can help improve the accuracy of predictive maintenance and reduce false alarms.

Predictive maintenance is particularly useful for factories as it can help identify potential energy efficiency improvements. For example, if a machine is operating inefficiently, predictive maintenance algorithms can identify the cause of the inefficiency and provide recommendations for improving energy efficiency.

This has several advantages over traditional preventative maintenance. Firstly, it can handle large volumes of data from multiple sources, which can be challenging to analyse using manual methods. Secondly, it can identify patterns and anomalies that may be missed by human operators. Thirdly, it can learn from past data to improve accuracy and provide more targeted recommendations.

To implement AI-enhanced predictive maintenance, a company would need to invest in sensors and data collection devices, as well as analytics software and machine learning algorithms. They would also need to develop a data management system to store and manage the data collected from sensors.

AI-enhanced predictive maintenance is an essential tool for improving asset reliability, reducing downtime, and maintenance costs. It is particularly useful in the manufacturing industry, where it can help identify potential energy efficiency improvements. By implementing AI-enhanced predictive maintenance, companies can ensure that their assets are operating efficiently and effectively, leading to improved productivity and profitability.

Challenges in Implementing Energy Efficiency Measures

Implementing energy efficiency measures in factories can be challenging due to various factors, such as the complexity of industrial processes, the cost of implementing energy efficiency measures, and the resistance to change.

One of the most significant challenges in implementing energy efficiency measures is the lack of real-time information about energy consumption and asset performance. Without this information, it can be challenging to identify areas of inefficiency and make data-driven decisions to optimize energy consumption.

Another challenge is the cost of implementing energy efficiency measures. Many energy efficiency measures require significant capital investment, which can be a barrier for some factories. However, it is essential to consider the long-term cost savings and environmental benefits when evaluating the cost of implementing energy efficiency measures.

Resistance to change is another challenge in implementing energy efficiency measures. Many factories have established processes and systems that have been in place for years, and there may be resistance to change. However, it is essential to communicate the benefits of energy efficiency measures and involve stakeholders in the decision-making process to overcome resistance to change.

Best Practices for Optimizing Energy Efficiency

To optimise energy efficiency in a factory, it is essential to follow some best practices, including:

Involve Employees: Employee involvement is critical to the success of energy efficiency initiatives. Employees should be incentivised and trained on the importance of energy efficiency and encouraged to adopt energy-saving habits. This can include simple actions like turning off lights and equipment when not in use, or more complex measures like implementing process changes that reduce energy consumption.

Utilise high quality IIoT Sensors: Industrial Internet of Things (IIoT) sensors can provide real-time data on energy consumption and equipment performance. This information can be used to identify inefficiencies and optimise equipment performance. Additionally, IIoT sensors can provide data for predictive maintenance, enabling proactive maintenance actions to be taken before equipment failure.

Implement Energy-Efficient Equipment: Upgrading to energy-efficient equipment can be one of the most effective ways to reduce energy consumption. This can include replacing outdated or inefficient equipment, such as HVAC systems, motors, and lighting, with newer, energy-efficient models. Additionally, implementing variable frequency drives (VFDs) on motors can optimize energy usage by adjusting the motor speed to match the required load.

Conduct Energy Audits: Regular energy audits can help identify areas for improvement and optimize energy usage. Energy audits can help identify equipment and systems that are consuming more energy than necessary and provide recommendations for optimizing energy usage.

Optimize Building Envelope: The building envelope, which includes the roof, walls, windows, and doors, plays a critical role in energy efficiency. Optimizing the building envelope can reduce energy consumption by minimizing heat loss or gain. This can include improving insulation, upgrading windows and doors, and repairing any air leaks or gaps.

Respond to Predictive Maintenance & Alerts: Once your predictive maintenance solution has been implemented, responding promptly and with the appropriate level of urgency to alerts sent by the predictive maintenance AI is crucial to ensuring that equipment is operating efficiently and identifying potential issues before they develop into failures. While predictive maintenance can help detect problems early, regular maintenance and inspections are also important for achieving optimal equipment performance and minimizing the risk of downtime.

Monitor Energy Performance: Tracking energy performance metrics can help identify areas for improvement and measure the effectiveness of energy efficiency initiatives. Key performance indicators (KPIs) such as energy consumption per unit of production, energy intensity, and greenhouse gas emissions can help measure the impact of energy efficiency measures and provide benchmarks for comparison with industry standards. Regularly monitoring energy performance can help identify trends and opportunities for further optimization.

IPMVP and IPMVP Baseline

The International Performance Measurement and Verification Protocol (IPMVP) is a framework for measuring and verifying energy savings from energy efficiency projects. The IPMVP provides guidelines and standard methods for quantifying energy savings, which can help ensure the accuracy and consistency of energy savings calculations.

One of the key components of the IPMVP is the baseline. The baseline is a reference point against which energy savings are measured. The baseline represents the expected energy consumption or demand if no energy efficiency measures had been implemented. By comparing actual energy consumption or demand to the baseline, the energy savings achieved by energy efficiency measures can be quantified.

The IPMVP baseline is an essential component of energy efficiency projects as it provides a standard method for quantifying energy savings. The baseline can be developed using various methods, such as historical data analysis, engineering calculations, or simulation models. The selection of the baseline method depends on the project's complexity and data availability.

The IPMVP baseline is typically established during the planning phase of an energy efficiency project. It is essential to develop a robust baseline that accurately reflects the expected energy consumption or demand if no energy efficiency measures had been implemented. A poorly developed baseline can lead to inaccurate energy savings calculations, which can undermine the credibility of energy efficiency projects.

Conclusion

Energy efficiency monitoring and management for a factory is essential for reducing energy costs and minimizing a factory's carbon footprint. Asset level analytics in real-time is critical for identifying inefficiencies and opportunities for improvement in individual assets. VFDs, HVAC systems, IIOT sensors, AI-enhanced predictive maintenance, and other tools can help optimize energy efficiency in a factory. Implementing energy efficiency measures can be challenging due to various factors, such as the complexity of industrial processes, the cost of implementing energy efficiency measures, and the resistance to change. However, following best practices, such as collecting and analysing real-time data, implementing energy-efficient equipment and systems, conducting regular maintenance and inspections, using AI-enhanced predictive maintenance, and involving stakeholders in the decision-making process, can help overcome these challenges and optimize energy efficiency.

Optimising energy efficiency in a factory is essential for reducing energy costs and minimising a factory's carbon footprint. Implementing energy efficiency measures can be challenging, but following best practices and utilising the latest tools and technologies can help overcome these challenges and achieve optimal energy efficiency.

With the increasing focus on sustainability and environmental responsibility, in some cases voluntary and in others regulatory, energy efficiency monitoring and management will only become more critical in the years to come as governments ramp up their regulatory requirements. Therefore, it is essential for factories to invest in energy efficiency measures and stay up to date with the latest technologies and best practices.

ROBERT EASSON, MBA, Founder | CEO of Easson Energy,?

is a technocrat and moderate conservationist with a career spanning over three? decades in the financial services sector (in particular: structured finance and insurance),?business intelligence, and big data analytics. He is also a serial entrepreneur who has?been involved in a number of successful ventures.

Mr. Easson's forward-thinking approach to energy efficiency included a strong focus on integrating business intelligence practices. This dedication led his team to receive the prestigious Microsoft Partner of the Year award for the creation of CO2 Scorecard. This powerful tool enabled major corporations, such as Telstra and ANZ Bank, to accurately baseline and report their CO2 emissions. By utilizing the precursor to the Power BI platform, CO2 Scorecard helped these companies improve their environmental accountability and take significant steps towards reducing their carbon footprint.

He is also a passionate energy efficiency evangelist who firmly believes that businesses can make a significant impact to reducing CO2 emissions by adopting energy-efficient practices.

Easson Energy

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