New Directions for Chemical Engineering (2022) / Chapter Skim
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8 Tools to Enable the Future of Chemical Engineering
8 Tools to Enable the Future of Chemical Engineering
Pages 199-225
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From page 199... ...
Some of these tools and capabilities currently exist in some form but will evolve in the future, some are emerging as this report is being written, and yet others will need to be created and will coevolve with chemical engineering over the next few decades. Some of these tools will emerge from current and future trends in technologies, with little or no involvement of chemical engineers in their development even though they will have significant impacts on chemical engineering.
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From page 200... ...
DATA SCIENCE AND COMPUTATIONAL TOOLS Chemical engineering has been a data-intensive field from the very beginning. The chemical industry was built on systematic measurement and cataloging of experimental measurements, including thermodynamic properties, phase diagrams, rate constants, flow rates, and heat- and mass-transfer coefficients.
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From page 201... ...
It is now possible to monitor every aspect of a chemical plant through a myriad of sensors capable of collecting data continuously, and it will become increasingly common to couple such "lower-level" process data to other types of "higher-level" data pertaining to global supply chains, raw materials availability and characteristics, failure data, responsible water management (Caballero et al., 2020) , or climate conditions throughout the world.
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From page 202... ...
New data science tools will enable ever-morerapid prototyping and scale-up, real-time assessment of performance in situ, and judicious adjustments based on continuous feedback from finite-element models running in parallel and in real time. New tools may one day make it possible to adapt manufacturing processes autonomously and in real time and to use immediately available raw materials based on local supply chain data.
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From page 203... ...
Such a future suggests profound and exciting opportunities for chemical engineers, who are trained in process integration and systems-level thinking -- skills that will be required to synthesize disparate data streams into information and knowledge. Artificial Intelligence AI is one of the modern tools of data science that is rapidly transforming all fields of science and engineering, including chemical engineering.
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From page 204... ...
Each year, an increasing number of chemical engineering graduates take nontraditional jobs as data scientists at such companies as Facebook, Airbnb, Microsoft, Google, Uber, Amazon, and more. In a growing number of instances, these companies are hiring chemical engineers for their data science teams preferentially over computer scientists and even data scientists because they recognize that a large part of "doing" data science lies in asking the right questions using existing AI tools rather than, for example, inventing new AI machines.
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From page 205... ...
fall meeting presentation titles and abstracts containing AIrelated terms. Between 2006 and 2020, the total number of presentations involving work using data science in some form increased from 0 to nearly 400, while the fraction of such presentations at a given meeting increased from 0 percent to nearly 7 percent (Figure 81)
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From page 206... ...
of American Institute of Chemical Engineers annual meeting abstracts that include terms related to data science, 2006–2020. Artificial Intelligence and Data Science Applications in Manufacturing Enabled by the internet of things (IoT)
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From page 207... ...
directions for chemical engineers in combining data science methods judiciously with the best aspects of traditional, physics-based models. An underappreciated aspect of data science with particular importance in manufacturing is data quality, curation, and provenance.
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From page 208... ...
Combining ML with molecular-dynamics simulations of nanoparticle self-assembly into complex structures, chemical engineering researchers are discovering microscopic details of assembly pathways that can be used to engineer products with fewer defects. In the future, both reaction engineering and assembly engineering will benefit from deep learning and other AI tools.
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From page 209... ...
It also means that for many chemical engineering problems, simulation and experimental length and time scales coincide. This makes it possible for experiments to inform model development directly and for computation to be a true equal partner with experiment in R&D.
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From page 210... ...
Another commercial driver of GPU technology is AI, with GPUs enabling the use of deep learning. The ubiquity of GPUs for deep learning has been accelerated by Tensor Cores, which not only speed up the linear algebra and large matrix operations at the heart of NNs but also carry out, as a single operation, multiple operations common in gaming.
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From page 211... ...
scans to self-driving cars. The continued applicability of Moore's law -- achieved by still-decreasing nanometer-scale chip features, continued densification and thus multiplication of transistors on single chips, continued densification of chips on computer boards, faster networking and switch speeds for faster communications and input/output, new and faster computing algorithms, declining costs, and ever-increasing accessibility -- will continue to enable chemical engineers to tackle bigger, more difficult, and more complex problems.
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From page 212... ...
, use MD and Monte Carlo simulation for inverse design of materials building blocks for targeted structures and properties. Rapid advances in computer technology and an unprecedented increase in computing power and data storage, combined with the ready availability of user-friendly modeling and simulation software packages powered by extremely efficient computational methodology, have facilitated modern modeling and simulation in chemical engineering.
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From page 213... ...
. Different parts of the chemical engineering curriculum will require different suites of modeling and simulation tools (e.g., MATLAB/SIMULINK for process control, ASPEN for process design, computational fluid dynamics [CFD]
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From page 214... ...
. The challenges for the future include better connecting simulation from one scale to another and developing interoperable simulation tools to inform model development for the systems scale.
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From page 215... ...
Industrial Applications Chemical Manufacturing Modeling and simulation have jointly played a significant role in the modern chemical and biomanufacturing industries, although applications have focused primarily on large-scale processes and equipment, mainly for operator training and for the design and implementation of advanced control systems. The increasing operational complexities and shrinking economic margins in the petrochemical industry, coupled with stringent environmental and quality demands on the manufacture of specialty chemicals and polymers, have spurred increased use of modeling and simulation.
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From page 216... ...
provide a brief review of potential bioprocess applications. With increasing capacity for collecting, curating, warehousing, and visualizing massive quantities of process data and with easy access to relevant ancillary data, the next challenge is to develop systems that will integrate process operating data seamlessly with ancillary data from the supply chain, policy, economic trends, global markets, and climate predictions to optimize production planning, scheduling, and operation.
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From page 217... ...
. Appropriate modeling, simulation, and data assimilation tools tailored specifically to facilitate such large-scale systems analysis do not currently exist, creating an area of opportunity for chemical engineers as they facilitate the transition from the linear to the circular economy.
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From page 218... ...
With rapid advances in biological/medical knowledge, data-collection capabilities, and computer hardware and software technologies, the development of such medical simulation systems may occur sooner rather than later. NOVEL INSTRUMENTS Chemical engineers have had a transformative impact on instrument development, especially in the establishment of fundamentals underpinning measurement and characterization, in the development of hardware, and in early adoption.
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From page 219... ...
The impact on biology and medicine has been particularly significant, since the high-throughput capabilities of these tools now make the routine collection of massive amounts of data a practical means of addressing biological complexity. The field of tool development offers opportunities for chemical engineers to contribute to the development of next-generation instruments that will provide both fundamental and practical insights not possible today.
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From page 220... ...
The design, analysis, and effective deployment of these systems offer opportunities for chemical engineers to contribute their expertise in fluid dynamics, reaction engineering, tissue and cellular engineering, and process systems engineering. SENSORS Sensors for Process Monitoring Sensors play an important role in the design, development, and monitoring of processes and systems in all areas of chemical engineering.
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From page 221... ...
An emerging opportunity is in the adoption of these sensors and sensor networks for remote operation and remote laboratories. While remote operations have been used in the past largely for processes with safety concerns or with inherently limited access, the practical limitations imposed by COVID-19, which made remote operations inevitable, have brought this technology to the forefront not only for manufacturing processes but also for instruction in laboratory courses, representing an area of potential future impact for chemical engineers.
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From page 222... ...
modifying existing analytical methods and using data science methods to determine the composition of complex mixtures (e.g., cell culture supernatant)
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From page 223... ...
. The ability to measure these key vitals through a wearable sensor has already transformed the collection of human physiological data, facilitating unprecedented acquisition of information about human responses in real-life situations in real time and continuously.
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From page 224... ...
. Extrapolation of current trends into the future suggests an ever-increasing capability of wearable sensors to collect even more massive amounts of data about human behavior in healthy and diseased conditions.
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From page 225... ...
While the pharmaceutical industry currently lags behind the chemical industry in its use of simulation tools, fundamental changes in regulatory requirements are motivating its greater use of mathematical models and simulation, especially in the rapidly growing biomanufacturing sector. Recommendation 8-1: Federal and industry research investments should be directed to advancing the use of artificial intelligence, machine learning, and other data science tools; improving modeling and simulation and life-cycle assessment capabilities; and developing novel instruments and sensors.
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