An industrial chemical company is using the power of machine learning to create a predictive system that predicts what chemicals it will need to make its products.
The company, Oxonox, is using a new AI program called K-Vista, which is based on DeepMind’s K-Nearest Neighbor algorithm.
Oxon, which specializes in developing chemical-grade chemicals, says the K-Vienna algorithm helps the company identify chemicals that need to be synthesized at low costs.
Oxonex says the algorithm uses machine learning algorithms to detect and detect the chemical compound, and then can use the predictive algorithm to create chemical products that could be used in products for industries such as food, cosmetics, and pharmaceuticals.
The machine learning technology has been used to predict the chemical reaction rate in a large number of industries and is already used to identify and make chemicals for food, pharmaceuticals, and other products.
Oxons founder, Mark Lohrman, says K-vienna can do things like predicting when the next batch of chemicals will arrive in a warehouse and also predicting how long the chemicals will take to be manufactured and how much energy it will require to produce them.
In this case, the company has developed an algorithm that predicts when it will be a good time to make the chemicals and can predict when it needs to make them, he said.
“K-Viena is a very interesting way of creating products that are cheaper and that are more efficient,” Lohrdman said.
The AI program is already being used in industrial manufacturing, and in manufacturing itself, Lohnders AI-based predictive algorithms can predict whether a product will be sold or not, and the results can then be used to make decisions about what to sell it.
Oxones AI-driven predictive software has already been used in making a chemical compound known as pyrrolidinium chloride.
Oxonian has already partnered with DuPont and General Electric to develop an AI-powered predictive software for a product called PPG-7, which makes plastics and paints, Lohns said.
Oxonal is developing the KVN algorithm for a chemical called 3-fluorouracil, which can be used as a biocontainment agent in vaccines and other medical treatments, Lonser said.
In addition to the chemical compounds, Oxonian also uses machine intelligence to predict what it needs for its manufacturing processes and for its supply chain, he added.
Oxonia is using K-vista in a number of industrial chemical processes, including the processing of petrochemicals, metals, and ceramics, and a range of industrial chemicals that have to be processed by a number that is based around a chemical reaction, Lones said.
For instance, the Oxonian K- Vienna program is used to generate a set of machine-learning algorithms to predict when certain chemicals will be required, Lies said.
As a result, Oxonia has created a model that predicts that it will not have the required chemical products for its factory and to get a better understanding of the problems it will have, he explained.
Lones says the company also uses K-vaenna to make predictions about when it has to get materials that will be used for its product manufacturing processes.
“It is just the type of thing that we are really focused on, and it is going to make a big difference in the end,” Lonsing said.
A similar predictive software is used by Dow Chemical, a chemical manufacturer, to predict how it can make products, Lorsons said.
Dow also uses AI-derived machine learning in its manufacturing process.
Oxonic is also using KVNs to predict, predict, and predict again.
“We are building the technology for industrial processes and in particular for chemical processes that are required in our chemical manufacturing,” Lohnstam said.
Lonsers software is already available in a beta version.
“Our software is designed to be a part of our predictive model so that the user can use it to create their own predictive models and that the users can see their predictive model as it develops over time,” he added, adding that the company plans to add more software to its product pipeline in the coming months.