Customer Story

Robust R&D speeds the quest for clean energy

Research scientists reduce both R&D cycle time and cost with design of experiments


National Institute for Clean-and-Low-Carbon Energy (NICE)

ChallengeMake clean coal technologies like carbon capture and sequestration a viable alternative to traditional coal-fired power.
SolutionAdopt an aggressive R&D agenda. Scientists and engineers at NICE have implemented a Six Sigma approach to ensure quality and efficiency in experimentation, relying on the robust analytical capabilities of JMP® to assist in designing multifactor experiments.
ResultsBy using JMP for design of experiments, NICE was able to cut R&D cycle time in half. More efficient experimentation has not only led to timely technological advances, it has also resulted in dramatic cost reduction across the R&D program.

Traditional coal-fired power plants today generate a majority of the world’s electricity. Growing concern over the harmful effects of coal-induced air pollution as a driver of climate change, however, has cast doubts as to the longevity of our global coal reliance. That’s why many of the world’s leading energy companies now recognize that clean energy is the path forward; many in the coal industry hope to find a clean way to continue utilizing existing coal resources without the harmful emissions.

Though “clean coal” methods such as carbon capture and storage and sequestration exist, the high cost of those technologies currently available means they have yet to be scaled up in any meaningful way. Even still, a clean coal future is today widely regarded as within reach. Technological innovation, it has been said, holds the promise of clean coal.

China’s National Institute of Clean-and-Low-Carbon Energy (NICE) is leading one of the nation’s largest efforts to make clean coal technologies feasible. The brainchild of China’s Shenhua Group, NICE has been tasked to develop the sustainable technologies that Shenhua sees as the foundation for an important new growth frontier.

“NICE has grown from scratch into an emerging powerhouse in the clean energy field,” says Dr. Yongwei Sun, Six Sigma Master Black Belt at NICE and expert member of the China Association for Quality. NICE runs an aggressive R&D program and innovation incubator aimed at inventing scalable coal-based alternatives. The goal of these technologies? To reduce emissions and waste from coal-fired power through new conversion methods.

Yongwei Sun

Yongwei Sun, PhD, advocates for Design for Six Sigma to improve the quality of R&D projects at China’s National Institute of Clean-and-Low-Carbon Energy (NICE).

Design for Six Sigma shifts the focus to efficient multifactor experimentation

At NICE, maintaining a proactive R&D program goes hand in hand with keeping abreast of the latest cutting-edge methods for research. Among these, Sun says Design for Six Sigma (DFSS) is his preferred approach to designing and redesigning processes and products to enhance quality. DFSS holds that strategic experimentation is crucial, and design of experiments (DOE) enables R&D scientists to make the furthest possible strides in science within time and cost constraints. “Once I joined NICE, I began to shift the focus to DOE,” Sun recalls. “It wasn’t long before other engineers began telling me they found DOE was extremely helpful for their research. There were a lot of solid results generated.”

Even so, Sun says that initially not everyone in the organization believed DFSS could deliver in R&D. “Most scientists did not want to adopt Design for Six Sigma because of psychological inertia. For example, a very senior expert with many years’ experience at first didn’t believe in DFSS at all. But then she found that one of her subordinates got very good results using DOE on a challenge that she previously believed was impossible to solve. And DOE made it possible.”

Prior to the adoption of a Six Sigma program, NICE’s research scientists were running experiments in the traditional way, one factor at a time. Optimal DOE required more robust tools to accommodate multifactor conditions, Sun says. That’s why the division adopted JMP®. “JMP removed concerns about DFSS and eliminated our scientists’ reluctance [to adopt the framework]. Now, in every DFSS project, DOE is a must.”

Statistical tools accelerate research findings

“JMP has a very good DOE module. It can help scientists reduce cycle time, reduce the number of experiments, save on cost and identify rules that could not otherwise be found by traditional methods,” Sun says. About 200 of the 260 employees at NICE are research engineers; more than 70 percent have earned a PhD and, Sun says, “Most of them are now using JMP.”

The results of a divisionwide DFSS rollout with JMP has produced tangible results, including a range of new scientific publications. Select titles include:

  • The Optimization of Sintering Processes for Alumina Extraction From Fly Ash.
  • Study of the Preparation Process Optimization of Calcium Silicate Board Using JMP.
  • Applications of Six Sigma Methodology in the Evaluation of the Pt/SAPO-11 Hydroisomerization Catalyst.
  • Parameter Optimization of Solid Amine Sorbents from Fly Ash for CO2 Capture.

In one example, engineers used DOE in JMP to study the effects of hydrogenation isomerization reaction parameters and the combustion of semi-coke with a gas heat carrier method. Six Sigma analysis showed that, for the latter, the main factors affecting the performance of low-order coal half-coke formation were pyrolysis and temperature. In another example, scientists designed a calcium silicate board, a useable industrial byproduct of alumina extraction, with DOE.

Moreover, Sun says, in the past scientists didn’t always consider variation in their experiments. “Now, with the help of JMP, when they do designs, they will consider variance and make the parameters robust to future production variation.” Another benefit is multi-target optimization. With traditional methodologies, it is almost impossible to find a design space that meets all targets. But JMP Profiler has made it possible, Sun says. “In addition to the fitted model, we also consider variation, cost and energy consumption, among others.”

Visualization helps researchers bridge the gap with decision makers

Scientists are finding that JMP not only augments research processes, Sun says, they are using the tool to create shareable graphics that make complicated technical results more accessible to less statistically savvy stakeholders. “The interactive graphics available in JMP are extremely important to us – in fact, this is one of the key differentiators between JMP and Minitab,” Sun says. Without JMP visualizations, “it would be very difficult for research scientists and engineers to show trends in the form of equations to their customers and managers. Images help explain the results in a way that generates interest among high-level executives – and gain their stronger support.”

Researchers reduced spending and cut R&D cycle time in half with design of experiments

Research outcomes are not the only tangible result of NICE’s DFSS rollout via JMP. “The most important savings is time savings. By our scientists’ estimation, the first DFSS project alone can reduce cycle time by 30 percent. By the second project, it cuts cycle time in half,” Sun notes. Moreover, Sun says internal records show that, on average, scientists have generated at least one patent application per DFSS project. It’s no surprise that “now there are more than 20 DFSS projects finished at NICE every year.”

In 2014, NICE received a China Quality Management Benchmark Award in recognition of the organization’s successful application of DFSS and TRIZ, a problem-solving method based on logic and data. Similarly, NICE received no less than five awards for Six Sigma excellence from the China Association for Quality.

“Without the help of JMP, I don’t believe we would have had such a high level of competency in R&D,” Sun says. “JMP has brought great value to NICE and to me personally. We have a great advantage over those research institutes that don’t use it.”

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The results illustrated in this article are specific to the particular situations, business models, data input and computing environments described herein. Each SAS customer’s experience is unique, based on business and technical variables, and all statements must be considered nontypical. Actual savings, results and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software.

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