AIoT, the confluence of AI and Industrial IoT technological forces, gives rise to a new digital solution category – the Artificial Intelligence of Things (AIoT). AIoT is built for industrial companies looking for better ways to connect their evolving workforce to data-driven decision tools and digitally augment work and business processes and making better use of industrial data already collected. ARC Advisory Group has observed that the convergence and overlap of IT and OT groups, driven largely by the digital transformation of industry in recent years has created organizational confusion and a significant "gray-space" of common technologies between each area, one area being AI. However, leveraging AI requires data science capability, which adds additional complexity to an already complex environment. While engineering roles are skilled in analyzing large amounts of data, setting up and creating production grade machine learning environments is not easily accomplished.

Optimized performance, reliability, availability and safety can be achieved with the automated operation. Digitalization in the mining industry is allowing mines to expand the utilization of technologies being used successfully both at the mine and plant levels, one of the most significant of which is mine equipment autonomy. While an exciting prospect, the mining industry is still having challenges as it works to integrate new technologies onto its mining equipment, such as shovels, drills, and trucks for capabilities like communications and positioning. Consider, too, that just because a mine has become automated does not mean that maintenance programs should be eliminated. On the contrary, they are perhaps more important than ever to support the optimized factors like availability and utilization of mine equipment now-automated workings as customers demand more than ever from their technology.

Mining is a traditionally analogue business. After all, the industry's symbol worldwide is a hammer and pick. Yet, despite the sector's antiquated reputation, some major mining companies are taking a progressive stance and proving digitisation and automation can achieve much better operational outcomes. Known as Mine 4.0, the industry is seeing digital transformation creep into everything from trucks, drills and trains to back-office processes, such as procurement and supply chain logistics. Miners have very little control over the revenue side of their business, as the global commodities crash of 2014 to 2015, when prices plunged by more than 30 per cent, and indeed the coronavirus epidemic demonstrate.

Aspen Technology, Inc. recently announced the general availability of the aspenONE V12 software release, which embeds artificial intelligence (AI) across the portfolio, and uses the cloud for delivery of enterprise-wide analytics and insights for increased safety, sustainability, and improved margins. AspenTech's Industrial AI solutions democratize the application of AI where it can deliver most value and is a vital step towards the Self-Optimizing Plant. "Aspen Hybrid Models are a major advance in the field of chemical engineering. Hybrid models are a major step forward in bringing together AspenTech's process models and machine learning and are a game changer in process engineering and plant improvement," said Dr. Karuna Potdar, Vice President, Technology Centre of Excellence, Reliance Industries Limited. Aspen Hybrid Models capture data from assets across the enterprise, and then apply AI, engineering first principles and AspenTech's domain expertise to deliver comprehensive, more accurate models at enterprise speed and scale.

Aspen Technology, Inc. announced the general availability of the aspenONE V12 software release, which embeds artificial intelligence (AI) across the portfolio and uses the cloud for delivery of enterprise-wide analytics and insights for increased safety, sustainability, and improved margins. AspenTech's Industrial AI solutions democratize the application of AI where it can deliver most value and is a vital step towards the Self-Optimizing Plant. Aspen Hybrid Models capture data from assets across the enterprise, and then apply AI, engineering first principles and AspenTech's domain expertise to deliver comprehensive, more accurate models at enterprise speed and scale. The process industries have embraced digital transformation to drive operational excellence and innovation as they respond to meet the needs of growing populations and expectations for sustainability. The new solutions in aspenONE V12 address these unique challenges with better modeling accuracy, greater insights and improved total cost of ownership that can support evolving business needs and take advantage of the new digital native workforce.

Aspen Technology, Inc. (NASDAQ:AZPN), a global leader in asset optimization software, today announced the general availability of the aspenONE V12 software release, which embeds artificial intelligence (AI) across the portfolio, and uses the cloud for delivery of enterprise-wide analytics and insights for increased safety, sustainability, and improved margins. AspenTech's Industrial AI solutions democratize the application of AI where it can deliver most value and is a vital step towards the Self-Optimizing Plant. "Aspen Hybrid Models are a major advance in the field of chemical engineering. Hybrid models are a major step forward in bringing together AspenTech's process models and machine learning and are a game changer in process engineering and plant improvement," said Dr. Karuna Potdar, Vice President, Technology Centre of Excellence, Reliance Industries Limited. Aspen Hybrid Models capture data from assets across the enterprise, and then apply AI, engineering first principles and AspenTech's domain expertise to deliver comprehensive, more accurate models at enterprise speed and scale.

Mining is a traditionally analogue business. After all, the industry's symbol worldwide is a hammer and pick. Yet, despite the sector's antiquated reputation, some major mining companies are taking a progressive stance and proving digitisation and automation can achieve much better operational outcomes. Known as Mine 4.0, the industry is seeing digital transformation creep into everything from trucks, drills and trains to back-office processes, such as procurement and supply chain logistics. Miners have very little control over the revenue side of their business, as the global commodities crash of 2014 to 2015, when prices plunged by more than 30 per cent, and indeed the coronavirus epidemic demonstrate.

If you ask in the geek atmosphere, it's very easy to get examples of how Machine Learning is applied in the tech industry. You will get answers like product recommendations, spam filtering, online fraud detections, computer vision, and many others. However, if you ask the same question about the applications in heavy industries, they definitely have to think about it a little bit to give you an answer. In addition, we all know Artificial Intelligence and Machine Learning algorithms are everywhere, we are using them while we use Netflix, Apple's Siri, Amazon's Alexa, Google's Waze, or the weather forecasting app. Therefore, in this article I am going to cover several applications of Artificial Intelligence for heavy industries, in each part, I will also give you examples of actual AI companies delivering that kind of machine learning products.

Organisations are looking to AI to revolutionise more than just their technology: it is redefining business processes, says Adi Pendyala, Sr Director at Aspen Technology. AI means different things in different situations and as such it can be tricky to define. Whilst most people think of AI as a technology in its own right, it is more of a general term used to refer to several different technologies that enable systems to act intelligently. When it comes to business applications, AI can support intelligent functionality by helping the system sense, understand, perform and learn. By using machine learning or deep learning to train a system, the system can assess how to act in each situation by analysing data, rather than relying on prescriptive, hard-coded actions.