As critical net-zero targets remain top of mind for today’s chemical and energy companies, technologies like solvent-based carbon capture can be deployed to reduce carbon emissions and advance sustainability goals.

As asset-intensive companies grapple with the increased complexity of maintaining reliable, high performing equipment, the challenge grows exponentially when critical asset data—maintenance records, sensor data, historical information, etc.—is siloed across various systems, departments and formats.

In today's complex and ever-changing technological landscape, integration between Information Technology (IT) and Operational Technology (OT) is not just a buzzword; it's a necessity.

As midstream companies continue to struggle gathering and understanding measurement data from disparate sources to make more informed decisions, new innovations in industrial data management are providing a simpler, smarter approach.

To effectively scale the hydrogen economy to meet ambitious decarbonization goals by 2035, companies must accelerate innovation, increase production and reduce risk. A “born digital” strategy is essential—from project inception across the entire process and asset lifecycle.

Are you still relying on simplified RAM analysis or even “gut feel” to make critical decisions for your operations? What if you could gain a more holistic view with in-depth insights to help quantify overall project value?

Today, the oil & gas industry is constantly faced with a dual challenge of meeting operational excellence and sustainability goals. The award-winning Aspen Generic Dynamic Optimization Technology (GDOT) improves margins and achieve sustainability objectives by vertically integrating planning, scheduling and advanced process control in closed loop.

For decades, first principle models have been used for process design and optimization in the chemical industry. Recent innovations employ machine learning techniques—enriched with first principle constraints—to create a hybrid model that closely represents real plant behavior. Fast, easy access to industrial AI is helping organizations make accurate decisions to select an optimum design quickly and improve operations, maximizing productivity and minimizing energy usage.

The successful execution of engineering projects requires intense collaboration among multi-discipline teams, but manual data handoffs often lead to errors, inconsistencies and delays that can derail a project.

Watch this webinar on Aspen InfoPlus.21® (IP.21) from AspenTech, exploring the potential of contextualizing historical data to transform your business.