Operating companies worldwide are relying on process simulation and modeling to improve plant energy efficiency. “There’s definitely no doubt that energy efficiency is a huge area of focus right now. There are different reasons for this and the reasons can differ according to the region of the world you are in. But wherever you are, it is a high priority,” confirms Ron Beck, productivity director for the engineering product line of AspenTech, Burlington, Mass.
At BASF, Ludwigshafen, Germany, modeling sits alongside experimentation as an essential part of the company’s R&D philosophy. “Most of our researchers complement their experimental work by modeling the underlying physico-chemical processes and simulating their experiments using appropriate commercial and/or in-house computational tools. The goal is to increase process knowledge about the fundamental reactions and to use the results, for example, for an optimization or for optimal experiment design,” says a BASF spokesman (Figure 1). With R&D efforts focused on the development of sustainable processes and products, minimizing energy demand is central to much of the modeling work.
“There are usually many possible options to improve energy efficiency. For example, increasing yield by using an improved catalyst will, in general, reduce the effort of recycling unconverted educts [reactants] or separating unwanted byproducts. Another possibility is to optimize and compare different process concepts in terms of energy efficiency. For a sustainable process development, the whole process has always to be considered,” the spokesman notes.
New or improved catalysts and polymer processes are two of the key areas of interest to the company; one of the commercial tools BASF uses is gPROMS Advanced Process Modeling from Process Systems Enterprise, London, U.K., a package specially designed to handle the difficult requirements of batch and semi-batch systems.
In one project, BASF used the software to build a high-fidelity detailed kinetic model of its batch expanded polystyrene process and then applied dynamic optimization techniques. The first-principles gPROMS batch process model included detailed reaction kinetics, with parameters estimated from experimental data. It modeled heat and material balances, geometry details, transport and thermodynamic properties, and plant operating procedures. BASF then used dynamic optimization to minimize batch time, taking into account process constraints. The company identified a 30% reduction in batch time, which, of course, provides energy savings.
SPOTTING POTENTIAL SAVINGS
AspenTech starts by running simple profitability models to discover energy-saving opportunities. Steady-state and dynamic modeling assess the demand side and Aspen Utilities Planner evaluates the supply side.
“These can be models of either facilities or operations, and we typically find that in a large plant you can improve energy efficiency by 10–25%,” Beck says.
However, one challenge with turning these opportunities into reality is the lack of in-house skills on modeling software — a legacy of management consultancies convincing chemical operating companies to outsource their engineering competencies, he notes.
For success, the models must be up-to-date and thoroughly understood by users.For this reason, AspenTech has set up a small consulting group to help operating companies. This group typically identifies at a large site 50–100 projects to improve energy efficiency, ranging from no-investment supply-side initiatives to ones requiring large capital investment.
A recent project at LG Chem’s Yeosu plant in South Korea illustrates the value of simulation technology. Here, management was charged with exploring reconfigurations to increase 1,3 butadiene production capacity and improve energy performance. Adding to the challenge, any process revamps couldn’t involve major upgrades or replacements.
LG Chem used Aspen Plus to develop detailed simulation models of the plant and employed the integrated Aspen Exchanger Design and Rating models to assess existing process equipment to determine available spare capacity.
Using the simulation models, LG Chem successfully developed methods to debottleneck both the extractive distillation column and fractionator — resulting in spare production capacity increases of 13% and 17%, respectively. Overall, the plant could achieve 15% greater capacity with only a small additional capital investment.
Similarly, the detailed heat integration of process streams produced by Aspen Energy Analyzer showed that rearranging the path of a solvent stream loop would enable the energy in the loop to be recovered and used for heating purposes elsewhere in the process. Moreover, this energy recovery decreased the final temperature of the stream before it went to a final cooler to 51°C from 70°C, which ultimately reduced the required quantity of cold utility in the cooler. In addition, identification of a new use for steam in the process eliminated excessive steam venting.
The company now is utilizing AspenTech products to simulate, monitor and optimize plant performance at other sites.