Heiko Claussen, AspenTech Co-Chief Technology Officer, discusses the role of Industrial AI in solving the data scientist shortage.

See how to transform customer experience with customer-facing solutions based on model-driven network data with AspenTech Grid Apps™.

See us in Booth #801 to explore cutting-edge technologies, business strategies and solutions at the premier industry event driving the future of carbon capture, utilization and storage.

Optimizing chemical processes requires input from multiple disciplines – engineering, costing, safety and environmental. Evaluating these factors early in the design lifecycle significantly improves the quality of the design and reduces time spent collaborating across teams.

Building a facies model that can incorporate complex facies and environments is always a challenge. Read how the unique Hierarchical Truncated Plurigaussian Simulation in Aspen RMS helps create stochastic facies models that simplify complex geological variables while delivering optimal performance.

AspenTech Industrial AI™는 공정 산업의 판도를 바꾸는 기술로서 AI 알고리즘의 속도와 성능에 실제 도메인 전문 지식의 효율성과 매개변수를 결합하였습니다. 특수 목적에 맞게 설계된 아스펜테크의 AI 솔루션은 데이터 인사이트, 엔지니어링 기반기술, 공정자산에 대한 지식 및 산업에 특화된 전문 지식을 결합하여 고객 기업이 끊임없이 변화하는 비즈니스 요구 사항에 더 빠르게 적응하고 대응할 수 있도록 지원합니다.

Natural gas is uniquely positioned as a transition fuel to support an affordable and secure energy transformation through displacement of coal power electricity generation and supplementing renewable electricity.

Process industries, especially refineries, must continually adapt to operational changes driven by variations in feedstock, product demands, and other factors. Moreover, many critical KPIs cannot be directly measured in real-time within an operating plant.

Frequent shifts in market dynamics and operational constraints make refinery decision-making increasingly complex. Traditional methods for refinery cost analysis and margin modeling struggle to keep pace, leading to inefficiencies and missed opportunities.

Developing automated 3D models during pre-FEED and FEED can drive down material costs, enhance asset visualization and help identify an optimal design early, minimizing revisions in later engineering phases.