One of the largest barriers to optimizing the design of a process plant is time. Too often, the design and engineering team simply does not have enough time to consider a sufficient range of options to land on the optimal configuration, sizing and composition of materials and equipment to meet owner requirements at the lowest capital cost. Of course, the design must also perform safely, not break down and consume as little energy as possible!

Variables such as the range of ambient temperatures, diversity of feedstocks, materials of construction and sizes of lines, tanks and equipment (to name a few) will always play a key role in determining optimal design parameters. But alas, that pesky project budget and schedule have the team "finishing" the design when the budget dries up or the clock runs out — and not when the optimal result is reached.  

This is where we think we can help. AspenTech is currently developing and refining two technologies that will assist engineers facing these challenges, empowering them to make better decisions and deliver an optimal design, faster.

Rapid Design Exploration

Typically, depending on the complexity of a project, the engineering team may run a few to a dozen design cases, each one requiring engineering cycles and man-hours, in order to fully understand the operating characteristics of a process plant. On the simple end of the spectrum, engineers will consider startup, shutdown, turnup, turndown, steady-state, summer and winter cases to configure, size and rate plant equipment.

AspenTech is currently working on software and access to high-performance computing that will help engineers consider thousands of potential cases from a wider range of ambient temperatures to more complex variations in the chemical makeup of crude oil and other feedstocks. This will help optimize the general design approach and enhance the productivity, efficiency and safety of the plant over its life.

Leveraging machine learning and high-performance computing, setting up cases and running these cases to define global optimums for design parameters can be done quickly within a single interface — and without undue time spent on the mechanics of comparing a vast number of potential cases and operating conditions.

 

Data-Driven Optioneering

A design team’s ability to impact final costs and the functional capabilities of a process plant decreases rapidly with time. Early decisions, such as how many trains will be used, how much buffer capacity is needed or how much redundancy to build into critical components, essentially lock in the design approach and limit subsequent alternatives.

However, decision-making for these critical early decisions can be painstakingly slow. And uncertainty around these decisions, which can be influenced by strong opinions or the experiences of one or more influential members of the design team, can slow the process even further and cause the schedule to slip.

AspenTech supports a more rigorous and data-driven approach to optioneering for both EPCs and owner-operators. For example, system risk and performance simulations can be leveraged to calculate the results of alternative design scenarios thousands of times to evaluate the likelihood of different outcomes and quickly make decisions that will improve cost, uptime and ROI metrics.

At AspenTech, we are looking forward to 2020 and the new decade and to helping our customers redefine the way that core activities such as multi-case analysis and early decision support are carried out. We have many irons in the fire here and are excited to share more details over the course of this year!

 

Learn more about how you can make a step change in engineering productivity and collaboration in our recent white paper, Optimize Asset Design and Operations With Performance Engineering.