Chemical process simulators were first introduced to the market in the late 1970s and early 1980s. Beginning with that original modeling breakthrough, process engineering has undergone significant transformation over the years, catalyzed by advances and innovation in software, both within individual disciplines and also in the integration across the workflow. This continuous evolution has created tremendous value for many companies, resulting in capital and energy savings, increased safety and reliability, and optimized designs with dramatic improvements in engineering quality and productivity.
This article outlines specific innovations and best practices, highlights examples of recent successes in the industry, and examines new approaches that are presenting additional opportunities for change in process engineering practice. The examples offered here span the asset-creation lifecycle, including R&D, early feasibility studies, conceptual engineering, basic engineering, equipment design, economic evaluation, energy management, debottlenecking and continuous improvement, and engineering support to manufacturing and planning.
A common aspect of all these examples is the huge impact that cross-discipline integration of modeling and analysis tools have on the selection of the best design options, the overall quality of the designs, and the safe and profitable operation of process plants. The ability to achieve superior energy and environmental performance while, at the same time, saving both capital and operating expense would not be possible without the capability of the models to rapidly analyze many alternative solutions and present design and cost tradeoffs to decision-makers. This is critical, as companies across the globe are facing an increasingly interconnected and highly competitive environment.
The process optimization opportunity
Process engineering plays a critical role across the entire asset lifecycledevelopment, design, construction, debottlenecking and operations. At the front end, process engineering decisions determine and constrain the ultimate economics of a facility (Fig. 1). In operations, process engineering decisions solve throughput, yield, regulatory and performance issues.
Fig. 1. Impact of integrated economics on
Given their highly varied role, process engineers benefit from interactions with other engineering and operational functions and disciplines on a daily basis. The exchange of ideas, recommendations, designs, analyses, plant data and process models of various kinds support optimizing increasingly complex designs and operations. By considering a broader range of ideas, companies are able to achieve improved asset performance, reduced costs, and increased safety and reliability.
Unfortunately, within many organizations, communication between engineering disciplines and with other functions is manual, sequential and inefficient. Understanding of the current state of the asset and opportunities for improvement are not consistently shared and executed across the groups. Organizational silos hold the expertise and data of each group and discipline largely within the group; this expertise is shared with other groups only after a design is released, or on a case-by-case basis, when it is important. This leads to significant loss of opportunitiesm, since ideas developed in one group are not fully exploited by other groups. Furthermore, the sequential nature of the interactions between engineering groups limits the screening of multiple alternatives during design and manufacturing, leading to suboptimal choices.
This sequential workflow results in a significant loss of opportunity in capital, energy and operating costs, and a missed chance to improve safety and reliability, which amounts to hundreds of billions of dollars per year in lost value. Improving this information-sharing and workflow presents a compelling opportunity to help asset owners and engineering companies to actualize the lost opportunities through a clear understanding of current asset performance and identification of optimum improvement opportunities that can be consistently executed across the lifecycle.
What follows is an examination of the journey of process engineering over the last three decades and the exciting innovations driving its evolution.
Impact of the desktop revolution
Following the introduction of process simulators (first on mainframes and mini-computers), personal computer (PC) price/performance was the next major breakthrough.
This had a dramatic impact on process engineering practice in the 1980s and 1990s. It lowered the barrier-to-entry to automate engineering calculations and democratized the ability to model and analyze an asset through process simulation and modeling.
The steady increase of PC power to solve bigger simulation models quickly moved the simulators from mainframes to each engineers PC. The evolving Microsoft Windows user environment spurred an evolution in ease of use of the models with graphical user interfaces, making them more accessible to a broader range of chemical engineers.
This accessibility has enabled process engineers working on plant problems to quickly establish an understanding of the current asset performance and rapidly consider improvement opportunities through the modeling of what-if scenarios. Expanded access to plant data and to manufacturing and planning tools helps process engineers translate improvement ideas into real benefits for the asset owners.
In parallel, desktop environments also made engineering design, cost estimation and analysis tools widely available and easier to adopt, enabling greater opportunities for idea creation and collaboration across disciplines. Desktop environments ultimately helped improve performance and increase productivity.
However, the authors have observed that, despite all of these democratizing trends, there is still a huge opportunity for all organizations to take advantage of these powerful modeling capabilities. To break through this barrier, we have conducted extensive usability studies and introduced radically new user interface concepts into several modeling tools, and we are continuing to do so across the gamut of engineering optimization products. Of this breakthrough user environment, BP Chemicals Dr. Godwin Tongo reports, The new paradigm has provided us with a big leap in flexibility and ease of use, to enhance and optimize our engineering productivity.1
Evolution of user interface and workflow paradigms has continued to accelerate, catalyzed by new innovations in software, hardware and mobile environments.
Convergence of modeling approaches
Another related area of improvement involves the convergence of steady-state modeling with dynamic modeling tools and the integration of sequential modeling with equation-oriented solution approaches. This has great significance, with the time-consuming efforts to build dynamic models and equation-oriented models for a complex process being overcome through building models, first in the steady-state mode and then reusing and building on them.
The ability to model processes dynamically is required to address the increasing complexity of safety, startup and quality challenges in highly optimized, large and integrated process plants, as well as for effective modeling of sequential batch units within process plants.
A recent example that demonstrates the power of this approach is the use of dynamic modeling together with relief system analysis tools for more accurate relief load and flare system analysis. This results in significant savings in capital costs related to flare capacity.2
Physical properties as innovation in process optimization
Web and software evolutions have enabled several areas of core chemical engineering innovation that provide the foundation for process modeling and optimization. Such innovations have been an integral part of achieving optimization benefits.
Today, a large and expanding set of highly accurate thermophysical properties is accessible to modelers. The example of a close-boiling distillation column (Fig. 2) provides a clear picture of the value of better thermophysical property characterization. A 5% error in vapor-liquid equilibrium (VLE) predictions can result in 100% error in capital cost estimation for the distillation column, which is a major capital item. Therefore, accurate physical properties are a key input parameter for reducing project capital and technical risk.
Fig. 2. Importance of accurate physical
properties: Estimating the cost of a close-
boiling distillation column.
Availability of physical properties data has always been a challenge in developing a new process or equipment design. Innovations in this area continue to accelerate engineering efficiency while improving the accuracy and reliability of model predictions and equipment sizing.
New optimization algorithms have continued to expand the scope and impact of process optimization. Improved analysis and visualization tools help engineers understand complex phenomena, enabling the development of more efficient processes. Continued focus on these innovations will be critical for value creation through process optimization.
Optimizing engineering through collaboration
In addition to innovations in process engineering, another aspect critical for value creation is collaboration among groups and disciplines to consider cost and energy parameters in the designs.
The traditional workflow for conceptual engineering today is sequential. During conceptual engineering, the main objective is to screen multiple design alternatives to ensure that an optimum design has been selected. A process engineer typically develops these alternatives using a process simulation tool. The most promising alternatives are then passed on to equipment (e.g., heat exchanger) specialists to size and design the equipment. The equipment specialist develops preliminary equipment designs and passes these to cost estimators. This sequence of tasks may take several days or even weeks to complete.
The sequential nature of the workflow slows down the overall process and limits the number of design alternatives that can be evaluated in the short window of opportunity available for conceptual engineering. One consequence is that economics and adequate equipment options are not considered early enough. The result is suboptimal designs and lost opportunities.
Fig. 3 shows the integrated conceptual engineering workflow that is now possible today. The integrated approach provides access to equipment modeling, sizing and economic analysis capabilities inside the simulation environment simultaneously, in a manner that a process engineer can use without being a specialist in design and estimating.
Fig. 3. Todays integrated conceptual engineering workflow.
This integrated approach allows the process engineer to rapidly screen multiple alternatives in a matter of hours instead of days or weeks. This saves 10%30% capital and energy compared to the traditional approach, since multiple alternatives can be rapidly screened and designs can be optimized early. BASF believes that its net benefits from the broad use of process simulation and conceptual engineering, in a comprehensive way, have been between 10% and 30% of installed capital cost of projects.3
Additionally, Dow Chemical reported savings of $65 million (MM) using integrated simulation and equipment modeling. The approach enabled identification of a debottlenecking opportunity in a chemical process while diagnosing and fixing a specific operational problem.4
Another newly accessible integrated workflow enables activated energy analysis directly from within the simulation model, so that promising conceptual options for energy savings can be identified during process design. Activated energy analysis, with equipment and cost analysis, enables process engineers to quickly identify the most promising options within their familiar process simulation user environment.
Using this innovation, Huntsman Chemical reported a reduction of 25% in energy intensity5; Honam Petrochemicals saw energy savings of 17.5% with the integrated conceptual engineering approach6; and S-Oil reported savings of $39 MM with payback of less than one year.7
Optimizing support to manufacturing and planning
A significant part of process engineering at operating companies involves supporting manufacturing and supply chain activities to troubleshoot and optimize assets. One of the key challenges is that engineering, manufacturing and supply chain teams do not share a common understanding of the current state of the asset and opportunities for improvements. As a result, initiatives to improve performance are often developed in silos and, in some cases, they compete with each other.
The process engineers focus is on understanding the process and predicting its performance. However, this is not shared effectively with the stakeholders in the manufacturing and supply chain. Communication tends to be ad hoc through a variety of mechanisms including emails, Excel spreadsheets, models, drawings and face-to-face meetings, among others. This prevents complete alignment across the key disciplines and, more importantly, results in lost opportunities for the asset owners.
Today, one may reuse a process model of the asset for what-if analysis, decision support and optimization of the asset. A process model encapsulates knowledge of the asset and provides the ability to reliably predict asset behavior. One approach to enable plant personnel to access the models is to use an Excel-based or real-time interface to shield model complexity.
Another opportunity involves reuse of process modeling information in production planning. This ensures that the production plan is based on accurate information of the current state of the operation and can correctly predict the optimization potential. Another area of innovation is the provision of real-time data for viewing and analysis within the process model itself. This provides a one-stop shop for troubleshooting operational issues.
Saudi Aramco has been using a combination of process models and production planning modelsreferred to as the Integrated Oil and Gas Modelto optimize exploration and production assets. This model is used for daily optimization and for planning purposes. Saudi Aramco has reported benefits of a 3%8% increase in production, a 3%5% reduction in energy usage, and a 50%70% decrease in planning time.8
New learning paradigms
A large number of new engineers are joining the process industry. This generation of engineers is rapidly changing the composition of the process industry workforce. There are considerable challenges in transferring an organizations intellectual property and knowledge, which are tied up in sophisticated models, to this new wave of engineers.
Discussions with key users have highlighted that, beyond the use of software, learning is also integrally tied to becoming experienced in discipline practices, such as developing conceptual design, flare systems analysis, and capital project estimating (among others); as well as in effectively training organizations to use the integrated workflows correctly. The software industry has responded to this need by introducing effective search tools and online training for engineering tools.
The prize for process optimization
An overview of the integrated process engineering workflow achievable today is shown in Fig. 4. The overall benefits of adopting a well-integrated process engineering workflow are a 10%30% capital and operating cost savings due to inherently better designs, a 10%20% improvement in engineering quality, and a 10%20% improvement in engineering efficiency. The integrated workflow enables process optimization and complements innovations in process engineering.
Fig. 4. An overview of todays integrated
process engineering workflow.
Process engineers play a key part in this workflow because of their understanding of the process. Their ability to model and optimize processes is at the core of value creation from the entire integrated workflow throughout the asset lifecyclefrom conceptual design to operations.
Considering the opportunities ahead
What are the key opportunities for process engineering? New innovations will continue to broaden the scope for process optimization through new, more accurate models for physical properties and process equipment, and through new optimization innovations. Further integration will enable process modelers to have better vision in optimizing process schemas against more parameters, including economics and sustainable operations.
Standalone design and analysis, such as equipment sizing and detailed equipment design, can be expected to play a more prominent role in the simulation modeling world. Advanced collaboration within engineering tools, combined with advances in engineering databases, will provide opportunities to better integrate global teams.
This journey is already beginning with the introduction of new process modeling search tools. IT innovations such as social networking, mobile and cloud computing platforms, and search technologies will transform process engineering once again.
Breakthroughs here can be expected to increase access to process modeling tools, reduce the learning barrier, and make optimization choices more visual and transparent. Web and cloud innovations will integrate people in addition to facilitating the integration of software applications. Process engineers will play an even more important role in this transformation due to their focus on understanding, modeling and optimizing processes. HP
1 Press release, AspenTech, March 6, 2012.
2 Feliu, J. A., Assessing Safer Blow-Down Options Using Dynamic Process Simulation, Inprocess Technology and Consulting Group, February 7, 2012.
3 Polt, A., Collaborative Conceptual Engineering at BASF, AspenWorld 2004, Orlando, October 2004.
4 Kolesar, D., Aspen EDR Helps Troubleshoot Thermosyphon Problems, AspenTech Global Conference, Boston, May 2010.
5 Smith, B., Huntsman Saves Millions of Dollars with Energy Efficiency Overhaul at Port Neches, TX, Chemical Week, April 23, 2010.
6 Park, J.-S., Site Energy Assessment and Total Site Study, OPTIMIZE 2011 AspenTech Global Conference, Washington, D.C., May 2011.
7 Kim, J. J., S-Oil Refinery Energy-Saving Project, OPTIMIZE 2011 AspenTech Global Conference, Washington D.C., May 2011.
8 Jones, G., J. Savla and D. French, Integrated Oil & Gas Model, AspenTech User Conference, Houston, May 2009.
|The authors |
||Dr. Vikas Dhole joined Aspen Technology in 1997 and serves as vice president of engineering product management. His responsibilities include strategy, direction and business performance of the aspenONE Process Engineering suite. Dr. Dhole previously held a variety of leadership positions in product management, technology development and consulting services. Prior to joining Aspen Technology, Dr. Dhole was technology manager with Linnhoff March Ltd. UK (now part of KBC Ltd.) and a lecturer at the Department of Process Integration at the University of Manchester, UK. Throughout his career, Dr. Dhole has championed innovative technology and software solutions in the areas of conceptual design, energy management and process engineering optimization. He has authored several publications on these topics. Dr. Dhole has a BTech degree in chemical engineering from the Indian Institute of Technology (IIT), Mumbai, India, and a PhD in process integration from the University of Manchester, UK.|
||Ron Beck is engineering product marketing manager at Aspen Technology. He has been with Aspen Technology for five years and is the marketing manager for the aspenONE Process Engineering suite. Mr. Beck spent 10 years in an R&D organization that commercialized fluidized bed technologies, enhanced oil recovery methods and environmental technology. He has 20 years of experience in the development, adoption and marketing of software solutions for engineering and plant management. |
He has also been involved with the development of integrated solutions for several global chemical enterprises, as well as Aspen Technologys economic evaluation products. Mr. Beck is a graduate of Princeton University.