Mitsubishi Chemical Group Corporation, Japan’s largest chemical company, faced quality problems in the company's polymer manufacturing processes that could not be detected on time, resulting in impacts to production. Using Aspen Hybrid Models with Aspen Plus®, Mitsubishi Chemical Group Corporation had the opportunity to create a robust and performant model that accurately predicted quality issues and supported preventive actions.

Stay ahead of unexpected equipment malfunctions and empower your team with AI and machine learning to accurately detect and prevent breakdowns.

AspenTech has created structured solutions packages – known as Sustainability Pathways – designed to help companies strategically meet sustainability targets across 11 of the most impactful areas.

Watch this video for an introduction of how Aspen Plant Scheduler and Aspen Mtell work together to predict and minimize the impact of downtime

Plant engineers often face disjointed workflows and a lack of critical information to make quick, informed decisions when troubleshooting equipment issues. Now, with the Alert Manager functionality within Aspen Mtell®, engineers have access to a centralized interface that allows them to identify the cause of the problem and prioritize the equipment criticality and failure mode severity for any alerts in queue through an interactive risk matrix. View this video now and discover how Alert Manager can help you to focus on the most critical issues, enabling you to improve equipment performance and increase profitability.

DuPont Selects AspenTech

Novozymes A/S, a global biotechnology company based in Denmark, was looking to support the development and optimization of biodiesel processes due to increasing biodiesel market demand, rising materials costs and more stringent industry regulations.

Achieve supply chain resilience with downtime scheduling that leverages prescriptive maintenance and advanced scheduling optimization to minimize impact on production.

A global leader in industrial data management from the shopfloor to the boardroom, we accelerate data-driven value creation in asset-intensive industries through robust data software offerings. 

Seismic data can provide useful information for prospect identification and reservoir characterization. Combining seismic attributes helps identify different patterns, for improved geological characterization. Machine learning applied to seismic interpretation is very useful in assisting with data classification limitations.