AI and IIoT are making heavy industry green
Heavy industries historically operated on the principle of producing goods in bulk. Their birth is traced back to the second industrial revolution, where the emphasis was put on quantities, rather than sustainability. One of the major impacts chasing us now is the gradual degradation of the environment, which is directly attributed to the emission of greenhouse gases, deforestation, use of fossil fuels and noise pollution, amongst many other negative effects. AI and IIoT are a viable solution to tackle some of the climate issues that result from heavy industries activities. Based on a report by Intel, AI and IIoT are seen as crucial tools in achieving environmental sustainability.
Simply speaking, AI is an upgrade from what we term as information technology. IT can be viewed as a basic building block, that is supporting the growth of advanced technologies and smart systems, which are able to autonomously operate and make adjustments to the changing processes with almost no human in the loop. As technology continues to evolve, so does its importance manifest in heavy industries. There are a number of ways in which we can leverage AI and IIoT to solve environmental issues that have persisted over the past decades.
Real-time monitoring of emissions and pollutants
From the onset of the industrial revolution, heavy industries have been relying heavily on fossil fuels to power their operations. These fuels are known to emit greenhouse gases, which researchers have attributed to global warming. These harmful gases, released from industrial processes, have a negative impact on biodiversity. According to a report by WHO, there are many people exposed to excess levels of pollutants, which are directly emitted from heavy industries.
AI and IIoT can be used as a tool to solve several issues associated with industrial emissions. For instance, smart sensors can accurately identify the amount of gases and precisely indicate the source of pollution. The recorded data within each region of concern could be shared between individuals, industries and government agencies to make a difference.
Management thinker Peter Drucker is often quoted as saying that “you can’t manage what you can’t measure.” As we now know what is happening, emissions and wastes can be harnessed to support other industrial processes. They might be subjected to chemical treatment to reduce the extent of emission, before releasing to the environment. Over time, the accumulated climate data can be subject to a more advanced data analysis, enabling the development of structured air quality control policies. The availability of such an inventory helps industrial establishments, as well as government agencies to develop predictive models, making it possible to create data-driven control regulations.
Using machine learning techniques, we can adjust and optimise industrial processes in order to achieve the highest levels of efficiency while reducing climate impact. The emission data can also be used to dictate the alignment of industrial plants, effectively plan the distribution of assets and advise on emission control methodologies for different industrial settings.
Tracking extent and impact of deforestation
Heavy industries are usually strategically located close to the raw materials while making the supply chain as effective as possible. Most of the assets are developed in areas with a high level of vegetation. Clearing large tracts of forest cover to pave way for road networks, clear spaces for setting up warehouses and workshop spaces poses a danger, in that the carbon sinks are cleared in large quantities.
Extraction of raw materials through methods such as open-pit mining similarly results in the clearing of a large territory. Such unsustainable practices pitch industrial players in legal battles with environmental conservation advocates and activists. According to FAO, deforestation is the second largest cause of climate change. It is estimated that forest cover, the size of a standard football field is lost to unsustainable deforestation every second.
Describing the industry as one of the least trusted, it is possible to draw parallels with the movie Avatar, which featured a company destructively mining a rare substance on a distant planet. It is a simple fact that mining is essential to human progress and has been for the last century and more. AI and IIoT are proving a crucial role in combating the negative impact of deforestation and ensure future development of heavy industries and their affiliated activities.
There are a number of ways in which AI and IIoT technology can be integrated into forest cover management in order to support the sustainable development of heavy industries. For instance, when an asset is set up in a remote location, forests may be cleared to pave way for development, while at the same time creating a loophole through which illegal deforestation and logging can be performed. In order to avoid this, some development in acoustic sensor technology is taking shape, which is able to detect any illegal felling of trees. A typical system that heavily relies on this approach to support forest conservation measures was developed by RFCx.
Drone technology, coupled with satellite imagery can be used as a tool to study forest covers, before, during and after the establishment of industrial practices within a forested zone. This allows the stakeholders to specifically identify the size of the forests, identify the dominant species and assess the growth patterns of forests. Through this method, they are able to gain an insight on how much cover is lost over a given project, they can then analyse the data collected to systematically decide on expanding or downscaling their operational practices.
Other companies are advancing AI and IIoT technology to incorporate humidity, soil condition and moisture level sensors in forests to gain real-time insight on the forests’ health. Through this data, we are able to analyse and predict events such as forest fires, drought or the general response of forests to changes in the size and intensity of forest covers.
Heavy industries similarly emit gases and particulates to the environment and can be dispersed over a wide area, way beyond the setup of the industrial establishment. These emissions mix with rainwater and atmospheric humidity, falling down as rain and can adversely affect vegetation as a result of acidic rain. The impacts of such an eventuality can result in stunted growth of forest cover and ultimately the death of some tree species. As such, integration of smart emission control mechanisms as well as control devices into industrial processes will avert possible deforestation.
Assessment of industrial energy needs
Heavy industries are culprits when it comes to energy consumption. The need for energy-saving approaches is growing as energy needs are directly linked to the profitability of companies. The capabilities of AI and IIoT in shaping the operations can be categorised based on energy needs, smart grids, handling power fluctuations and advancing energy storage strategies.
Smart grids integrate machine learning to identify energy consumption patterns during the various times of the day and perform autonomous energy audits for industrial plants. By applying a broad range of technologies, industries are now able to collect data in real-time, perform analytics and develop a consumption model. The model is then used to optimise energy use and diversify the supply sources, to meet every specific aspect of operations. An advanced AI and IIoT platforms will allow heavy industry managers to switch between renewable energy sources. During peak hours, when the industry is operating at full capacity, smart grids will maximise usage of power from a renewable energy source, cutting down the need for power from conventional sources such as coal-powered plants or diesel generators.
The growing production calls for the utmost conservation of energy resources. This has led to a big investment in the development of renewable energy technologies, namely wind and solar, which may at times produce excess energy during their peak operating time. The excess energy has to be stored for future use when generation from these sources are at a minimum. AI and IIoT help heavy industries to study generation and usage patterns, perform forecasts on renewable energy availability and perform predictive maintenance on storage and distribution sources themselves.
Real-time condition monitoring and maintenance of heavy industries
Accidents in industries that occur from faulty equipment result in adverse environmental pollution, especially if it involves the release of effluent, toxic chemicals, radiation or materials that pose a direct threat to biodiversity. There have been marked improvements when it comes to industrial safety and operational maintenance strategies, but past accidents serve to remind us of the need to always innovate for the overall safety. An approach, followed by most engineers to avoid large scale accidents is performing preventive, predictive and proactive maintenance strategies on industrial plants and processes.
AI and IIoT allow maintenance personnel to gather sensor data from the equipment under scrutiny, previous maintenance data and history or pattern of maintenance, in an approach referred to as condition monitoring. Sensors embedded on key areas and equipment in an industrial environment, collect and store crucial performance data, provide immediate feedback and allow the maintenance teams to perform proactive maintenance actions, therefore avoiding cases of failure or accidents. Condition monitoring can take the form of vibrational analysis, acoustic analysis, infrared thermography or even lubrication conditions.
Using AI and IIoT to control waste
Since AI and IIoT build on the basics of information technology, computing and electronics, it is expected that as technology advances, so will the quantity of e-waste grow. AI and IIoT are at the forefront in combating the grave dangers associated with uncontrolled waste disposal, by supporting the development of sustainable materials, which accelerates the integration of eco-friendly hardware. This can also be achieved by upscaling smart recycling of e-waste. Hardware monitoring approaches can also be developed, with the methodology helping to notify the user on the effective life cycle of hardware and diagnose all possible failures.
Waste and sewage streams of heavy industrial plants can also be equipped with sensors that identify the type and level of toxic material in waste before disposal to the urban sewage system. It also advises on best practice, to be followed to salvage useful waste that can be used for other applications, e.g. converting bagasse as fuels for power generation.
AI and IIoT will play a very crucial role in promoting the sustainability of existing and future operations. Ethical application of these technologies will positively result in the remediation of the environment, fighting adverse effects of climate change as a result of industrialisation and integrating sustainable practices into processes, harnessing of raw materials and the entire supply chain.
Challenges in implementation and execution of some technical changes associated with AI and IIoT may exist, mainly in the form of legislation, integration of technology and overall cost of implementation. Successful implementation of AI and IIoT in heavy industries will most definitely boost the autonomy of processes, promote product quality and assure sustainability.
