News
PDT Goes Green as Joint Venture SinoCAN Intellitech Inc. Becomes Exclusive Reseller in China For Lakes Environmental Software
Waterloo, ON., August 21, 2007 - Pattern Discovery Technologies Inc. (PDT) announced today that their Chinese joint venture company SinoCAN Intellitech Inc. has entered into a distribution agreement with Lakes Environmental Software. SinoCAN Intellitech Inc. will be the exclusive reseller of Lakes Environmental's software products in China. SinoCan will also provide training and support. Lakes Environmental is the developer of air dispersion modeling software as well as a number of other environmental IT solutions.
"We are very pleased to have formalized our relationship with Lakes Environmental," said Mr. Bin Wu, CEO of SinoCAN Intellitech Inc. "Lakes' software and environmental IT solutions enhance our ability to meet our clients' needs in the area of environmental management, in addition to our Production Intelligence solutions for plant optimization. We are looking forward to a long, mutually rewarding relationship."
Lakes Environmental is committed to developing robust and easy-to-use, state-of-the-art GIS based software systems for several environmental related areas such as air pollution modeling, human health and ecological risk assessment, and emissions inventory. Their software systems are used worldwide by government, academia, and consultants. Lakes Environmental is headquartered in Waterloo, Ontario, Canada.
Founded in 1997 as a spin-off from the University of Waterloo, Pattern Discovery Technologies Inc. is a pioneer in data mining and predictive analytics. PDT has developed Production Intelligence, a process based on proprietary technology and core competence in data mining and predictive analytics. Production Intelligence is ideally suited to industrial process scenarios that typically involve large, complex, disparate data sets.
PDT and Dahua Engineering Group Join Forces to Bring Production Intelligence and "Industrial Analysis Made Easy" to China
Waterloo, ON., June 1, 2007 - Pattern Discovery Technologies Inc. (PDT) announced today that they have entered into a joint venture with Dahua Engineering Management (Group) Co. Ltd. Of Beijing, China.
The venture will effectively combine Dahua's presence and experience in China with PDT's Production Intelligence technology and services. Production Intelligence is PDT's proprietary, data driven approach to ‘analysis made easy' for complex industrial processes. The joint venture will initially be working with clients in the conventional oil and petroleum refining industry with a vision for future stakes in the power generation, coal mining/coal power and electrical grid arenas. The focus of the joint venture is to bring opportunities for continuous process improvement , increased efficiency and assistance in meeting environmental management demands, to their clients in continuous operation complex industrial process industries in China.
"From past experience we have learned that it is crucial to have an experienced partner on the ground in China. We are confident that in joining forces with Dahua we have solidified our position with a reliable and knowledgeable partner. We are very excited about our current projects and our future opportunities," said Dr. Yang Wang, VP and CTO of PDT.
Dahua Engineering Management (Group) Co. Ltd. was founded in 1997. Dahua specializes in engineering consulting and management. They have supervised over 26 key power engineering projects as well as environmentally comprehensive ecological projects in 22 provinces. Dahua also played a key role in natural gas projects including a five year development plan for natural gas vehicles. Dahua currently has 224 senior engineers and 603 engineers in 10 branches across China.
Founded in 1997 as a spin-off from the University of Waterloo, Pattern Discovery Technologies Inc. is a pioneer in data mining and predictive analytics. PDT has developed Production Intelligence, a process based on proprietary technology and core competence in data mining and predictive analytics. Production Intelligence is ideally suited to industrial processes such as petroleum production and refining, that typically involve large, complex, disparate data sets.
"Industrial Analysis Made Easy" - PDT and Major Canadian Oil Sands Operator Sign Multi-Phase Contract
Waterloo, ON., May 28, 2007 - Pattern Discovery Technologies Inc. (PDT) announced today that they have entered into a multi-phase contract with one of Canada's largest oil sands operators.
PDT will implement their proprietary Production Intelligence process to measure, monitor and manage plant performance. PDT's data-driven "industrial analysis made easy" approach will allow the client to easily perform complex data analysis without the need for data analysis experts or data mining expertise. PDT's PI process includes design and implementation of a data framework that includes data from all functional silos, providing a holistic, plant-wide view to process data. The data framework is built on a solid foundation of client domain expertise and PDT's core strength in data analysis. Combining these strengths, this powerful framework brings meaningful context to data allowing for robust analysis.
PDT will also provide custom tools and applications that will enable plant managers and operators to quickly and decisively determine why upsets occurred and will provide guidance for corrective action. As a result of this project, the client will be able to fully leverage their existing automation system investment as well as exploit their data asset, eliminating reactive process management and enabling proactive decision-making in real time.
Founded in 1997 as a spin-off from the University of Waterloo, Pattern Discovery Technologies Inc. is a pioneer in data mining and predictive analytics. PDT has developed Production Intelligence, a process based on proprietary technology and expertise in data mining, analysis and predictive analytics. Production Intelligence is ideally suited to continuous industrial process scenarios, such as oils sands production, that typically involve large, complex, disparate data sets.
