Metrics for Sustainable Technology Innovation

In today's rapidly evolving technological landscape, measuring performance is essential for sustainable technology innovation. Applying science and engineering principles to new problems drives progress and ensures technical and economic viability, while ensuring more sustainable solutions to today’s problems. Metrics provide a structured way to track these criteria, validate new technology against the competition, and report out to key stakeholders. These metrics help us evaluate reaction and separation tradeoffs, optimize reactor systems, and ensure that our processes are both economic and sustainable.  Some examples of key metrics include: 

Reactors are the heart of any chemical, biological, or electrochemical process, and scaleup of new reactor systems have their own subset of critical metrics.  By rapidly transitioning from small-scale to pilot-scale reactors, we can gather valuable data that can be used to scale directly to commercial production. Key design metrics such as mass transfer rate (kLa) and weight hourly space velocity (WHSV) are essential for optimizing reactor performance and ensuring economic viability,  and can be used as scale independent parameters to track performance at any scale. For instance, for many gas/liquid reaction systems, including gas fermentation systems, kLa (mass transfer coefficient) is evaluated to set minimum targets for commercial design, ensuring that the mass transfer rate is sufficient for economic viability. Similarly, WHSV (weight hourly space velocity) is a key measure of liquid flow per unit of catalyst, which is crucial for reactor performance.

Metrics used for design, scale-up, and operation of reactor systems in thermochemical and bioprocessing systems can include: 

Case Studies and Examples

To illustrate the practical application of these concepts, let's look at a few case studies:

1. Sustainable Bio-based base oils: Base oils are typically produced as a petrochemical fraction, and are used to produce lubricants, greases, and other heavy oils for industrial use.  In this example, metrics for reactor scale-up were applied to develop sustainable bio-based replacements for petrochemicals. The key scale-up metric used was the Reynolds Number, a dimensionless parameter that describes properties for both the liquid phase and gas phase over a solid catalyst.  Using standard guidelines for Reynolds number correlations in trickle bed reactor systems, this ensured that we could identify reactor conditions in the regime known to promote sufficient mass transfer for good performance.    
   
   
   
   

2. Biobased Monomer Production: Another example involves the production of a biobased monomer. The process rapidly transitioned from a 50 mL to a 1500 mL pilot scale, achieving a 30x scale-up. The pilot results could then be used to scale directly to commercial production. In this case, the key scaleup parameter was weight hourly space velocity (WHSV), a parameter than determines the catalyst volume needed for a given reactor performance.  The table below shows a typical scaleup plan that could be used for this type of scaleup problem. 

3. Novel Bioreactor Design: In this case, a novel bioreactor was designed for gas fermentation. The key design metric, kLa (mass transfer coefficient), was used to set performance targets and design the equipment.  Published correlations were used to evaluate the mass transfer performance vs the superficial velocity of the gas phase through the reactor system.   The minimum reactor performance determined the reactor design parameters necessary for an economic process design. 

In conclusion, metrics play a pivotal role in the successful design, scale-up, and optimization of sustainable technologies. The case studies presented in this document illustrate the practical application of these concepts, demonstrating how metrics can guide decision-making and ensure that our processes are efficient, economical and sustainable.

Navigating the Bumpy Road of Industrial Biotechnology Scale-up

We have seen a growth in products from Industrial Biotechnology, with commercial technologies emerging in areas such as:

• Fuels. Sustainable Aviation Fuel, Green Diesel, and Ethanol from low cost feedstocks

• Chemicals. Drop-in replacements for industrial chemicals such as propanediol and butanediol, made through biological routes instead of conventional petroleum based options

• Alternative routes for proteins, fats, and meat

• Materials for building projects, fabrics, electronics

The drivers for this growth include a focus on sustainability, and a drive to enable circularity through reuse of carbon and carbon-based products. Some technologies offer the potential to make use of a lower cost source of carbon, through use of waste feedstocks such as industrial gaseous emissions, biogas, end of life plastic, and waste biomass. In addition, in some cases the bioproduct is a better product than the petroleum-based version, coming with a cheaper, safer processing route and performance advantages over traditional materials.

The road we travel while commercializing new technologies like these is often bumpy, with many challenges along the way. In order to be successful we must address these challenges while also: 1) reducing technology risk 2) reducing time to market 3) optimizing/minimizing cost and 4) maximizing value.

These are often competing objectives, and usually reducing time to market and reducing risk win out. Of course, if the capital and operating cost are too high, then a new technology will not be successful, so these criteria cannot be ignored.

We can follow guidelines and best practices for the scale-up and design of industrial bioprocessing technology, to effectively de-risk and optimize new industrial biotechnology during the scale-up effort. These guidelines include elements such as:

Creative Process Engineering: The flow scheme is developed, the material balance is estimated, and key process design decisions are identified to establish the best process flowsheet for the technology.

Modeling & Analysis: A good model can save time and resources in the lab. Coupled with the right analysis, the scale-up team can prioritize objectives in the lab, pilot, and demo units.

Experimental Data: The right data is needed to prove out breakthrough ideas, secure partners and investors, and develop engineering data for equipment design. Multi-scale data is critical to this effort, and with good planning, multiple assets and external resources can be leveraged.

The key benefits of this approach are:

• Prioritization of R&D to de-risk and optimize the new technology.

• Identification of cost reduction opportunities throughout the scale-up effort.

• Anticipation of process design needs as early as possible.

While scale-up of new sustainable technology, in particular industrial biotechnology, is hard and challenging, it is not impossible! The opportunities are great, and with the right approach we will see many more success stories in the future.

Growing the Bioeconomy with Gas Fermentation

 Gas fermentation is a novel industrial biotechnology that can contribute to the growth of the bioeconomy by using low cost, readily available carbon sources such as methane, carbon monoxide, and carbon dioxide to produce various fuel, chemical and food products, such as ethanol, ethylene, triglycerides, proteins, and polyesters.

Figure 1:  Gas Fermentation Landscape

Gas fermentation has advantages over conventional processing routes, including:

·       lower cost operating conditions

·       robustness to fluctuations in feed rate and composition

·       tolerance to contaminants in the gaseous feeds. 

We can look at two classes of gas fermentation.  The first involves direct conversion of CO2 through gas fermentation, typically with hydrogen and/or oxygen as co-feeds.  A diverse array of products can be produced through these routes, including triglycerides which can be used for food, materials, and fuel applications. Chemicals such as acetic acid and ethylene are other products viable through these routes, along with single cell proteins for animal feed or other alternative protein applications. 

We can also consider gas fermentation routes that convert CO2 precursors, such as carbon monoxide or methane into useful products.  In this case, possible products include chemicals such as ethanol, methanol, or iso-propanol, and polymers such as polyhydroxyalkanoate (PHA).

However, a key challenge with gas fermentation involves the design of a cost-effective reactor system with high mass transfer coefficients for the gaseous feedstocks into an aqueous media.    A number of reactor types have been proposed to overcome this challenge, from simple bubble columns to more sophisticated air lift and external loop reactors. These reactor types have tradeoffs between mass transfer and design complexity.  It is important to identify the best option for a particular gas fermentation application. 

Figure 2:  Mass Transfer Challenge

In addition, with any biological or chemical process it is important to look beyond the reactor system and consider the integrations of unit operations both upstream and downstream of the reactor system in order to optimize the process as a whole. For gas fermentation, we may need to consider tradeoffs associated with the cost of compression or gas cleanup vs potential performance benefits in the reactor system. Similarly, we need to consider the design and performance of the product recovery section. Ultimately, we want to optimize the process not just the reactor system.

Figure 3: Process Integration Challenge

Additional challenges must be addressed when scaling and commercializing gas fermentation technology.  These include:

·       Lack of established data and models.  Compared to petrochemical reaction chemistry, the availability of data and reactor design models is quite limited. 

·       New equipment to be designed and constructed, such as custom fermenters.

·       New separation challenges.  Recovery of extracellular products such as ethanol or acetic acid from the fermentation broth, or recovery of intracellular products.

·       New optimization criteria.  Carbon footprint and ESG/LCA metrics in addition to traditional optimization metrics such as operating cost and capital cost.

·       New microbial catalysts.  As gas fermentation becomes a more mature and broadly deployed technology, methods for manufacturing and distribution of commercial scale quantities of these catalysts will be required. 

As gas fermentation becomes more mature and we see more commercial applications, opportunities for future developments will enable greater scale, reduced production costs, and new products.

·       Microbial modeling, including bacterial growth kinetics and flux models.  By bringing a more analytical approach to our gas fermentation systems, we can enhance understanding of the biological reactor systems, and develop custom reactor designs for specific microbial systems. 

·       Strain development to reduce bioproduct formation, increase contaminant tolerance, and enable more extreme operating conditions (higher temperature, for example). 

·       Reactor design and scaleup, to develop reactor systems that can enhance mass transfer while balancing constraints around capital and operating costs. 

·       New or improved approaches for product recovery to reduce the cost and complexity of product separation and purification. 

The future is bright for this exciting technology area. Gas fermentation will play a key role in the growth of the industrial bioeconomy in the coming decades. 

Guiding Scale-up of Sustainable Technologies

an interview with Michael Schultz published first at Proofing Future: Bridging People + Ideas on 15 Aug 2022

“I help companies navigate the bumpy road of scale-up, providing process development services to validate key technical concepts, optimize the right parameters, and derisk the technology. I also enjoy working with clients to build capability. Going beyond producing deliverables, I view a project as a success, if I can transfer my knowledge and learnings so that I am finally no longer needed,” says Dr. Michael Schultz, Principal at PTI Global Solutions.

Dr. Michael Schultz enjoys the challenge of solving difficult engineering problems to reduce carbon footprint and to deliver economic value. As Managing Director of PTI Global Solutions, Michael works with companies in the sustainable technology space to help accelerate commercialization, reduce risk, and get the greatest value from these great ideas. 

Previously, Michael held positions at LanzaTech, Battelle Science and Technology Malaysia, and UOP, leading process R&D and scale-up across a broad range of chemical and biological technology areas.  Michael holds a B.S. in Chemical Engineering from the University of Michigan and a Ph.D. in Chemical Engineering from the University of Massachusetts.  He received the 2015 EPA Greener Synthetic Pathway award from the US EPA and the 2005 Haden Freeman Award for Engineering Excellence from IChemE.  Mike has been granted more than 45 US Patents in his career.

Sign up to meet Michael Schultz on August 25 to discuss “Financial Instruments for Scale-up of Sustainable Technologies”

Contact Michael Schultz with your interest in Scale-up of Sustainable Technologies.

Sebastian Klemm: You recently presented “Practical Guidelines for Scale-up of Sustainable Technologies” at the Process Development Symposium in Philadelphia. What are the cornerstones of these guiding principles of yours?

Michael Schultz: The challenges with scale-up of sustainable technology are to effectively reduce the time, cost and risk of scale-up. These are often competing objectives.

Typically, the scale-up timeline is the most critical of these. Often we must accept some risk to move quickly. The key is to understand risks, mitigate where possible, perhaps with some strategic overdesign and investing in R&D at all stages of scale-up.

To effectively move quickly while managing risk, we need creative engineering, effective experimental programs with critical data gathering at each stage, and useful modeling efforts. 

Sebastian Klemm: How can you concretely support companies through scale-up challenges of new products & process technologies?

Michael Schultz: I help companies navigate the bumpy road of scale-up, providing process development services to validate key technical concepts, optimize the right parameters, and derisk the technology.

I am currently working with a company producing a novel polymer additive as they look to scale-up their technology. Having proven product manufacture at the lab scale, I am working with them to define and build their process technology at larger scales.

I also help clients better assess and understand their technology, by providing an internal engineering review of their technology, and guiding the team in a scale-up risk assessment. 

I also enjoy working with clients to build capability. Going beyond producing deliverables, I view a project as a success, if I can transfer my knowledge and learnings so that I am finally no longer needed! 

Click to visit the PTI Global Solutions website

Sebastian Klemm: How do you think the Inflation Reduction Act just passed in the U.S. can help turn the tide in favor of investing in sustainable technologies and clean energy solutions?

Michael Schultz: The Inflation Reduction Act[1] provides a number of financial mechanisms to stimulate further adoption of clean energy and sustainable technology in the United States.

The IRA is a predominately technology neutral approach, providing incentives across sectors such as power (wind, solar, nuclear), electric vehicles, transportation fuels, clean hydrogen, buildings and households. The IRA also provides much greater incentives for US manufactured content and prevailing wage requirements, providing a mechanism for US based manufacturing jobs to support this growth in clean energy.

Sebastian Klemm: Could you elaborate on how the scale-up of sustainable technologies interfaces with financial instruments?

Michael Schultz: The most successful sustainable technology scale-up efforts rely on many financial instruments to support scale-up and commercialization.

This can include grants from various public and private sources, joint development partnerships, venture capital funding, and for larger, first of its kind demonstration or commercial projects, specialized loan programs to support project capital investment.

In the past, I have provided technical due diligence support for various organizations making financial transactions in sustainable technology. This has included the US Department of Energy – reviewing applications for funding to support pilot and demonstration projects for the production of next generation biofuels, the US National Science Foundation – evaluating applications for early stage projects for sustainable chemicals, and a Special Purpose Acquisition Company (SPAC) who was pursuing an acquisition of a commercial, or nearly commercial stage company in the sustainable technology space.

I am currently working with an early stage company with a novel technology for producing petrochemical alternatives from renewable feedstocks. I am helping them develop a process design for a pilot plant, and using this as a basis to estimate a preliminary, order of magnitude capital cost. This cost estimate will enable my client to plan and evaluate options to determine the best approach for financing for this project.

For each of these assignments it has been critical for me to assess the state of the technology in question, review technical and financial projections, and provide an assessment of any technical risk that should be taken under consideration.

Sebastian Klemm: You just recently completed a book chapter themed “Process Scale-up for Bioproducts: Enabling the Emerging Circular Economy”. Which particularities and crunch points do you address?

Michael Schultz: This chapter addresses the emergence of fuel, chemical, and food products produced from biobased feedstocks, using biobased catalysis and a combination of both of these elements. Similarities and differences in the scale-up of bioproducts compared to the scale-up of more conventional thermochemical processes using petroleum-based feedstocks will be presented, along with a case study and commercial success stories for the scale-up of bioproducts.

We see opportunities to contribute to a circular economy by producing products from renewable feedstocks and waste carbon. Challenges associated with the scale-up of bioproducts include:

• the availability of feedstock

• the lack of established data and a knowledge base for these new technologies

• new optimization criteria that include metrics such as carbon intensity and other environmental, social, and governance (ESG) factors

However, with recent success stories such as the growth in drop-in, bio-based transportation fuels such as Sustainable Aviation Fuels (SAF) and renewable diesel, new bioproducts such as polylactic acid (PLA) and bio-based replacements of conventional petrochemicals such as  1,4 butanediol and 1,3 propanediol (precursors to many polymers and other materials we use every day), the future is bright for continued growth in bioproducts.

For instance, the European Bioplastics Organization estimates that the global production of bioplastics will increase from 2.4 million tons in 2021 to 7.5 million tons by 2026.[2] Production of bio-based transportation fuels[3] such as biodiesel, renewable diesel, ethanol, and Sustainable Aviation Fuel (SAF)[4] is expected to increase as well in the coming years.

Sign up to meet Dr. Michael Schultz on August 25 to discuss “Financial Instruments for Scale-up of Sustainable Technologies”

Contact Dr. Michael Schultz with your interest in Scale-up of Sustainable Technologies..

References

↑1https://en.wikipedia.org/wiki/Inflation_Reduction_Act_of_2022↑

2https://www.european-bioplastics.org/global-bioplastics-production-will-more-than-triple-within-the-next-five-years/↑

3https://www.iea.org/data-and-statistics/charts/global-biofuel-production-in-2019-and-forecast-to-2025↑

4https://www.icao.int/environmental-protection/pages/SAF.aspx

Manage Risk While Scaling Green Chemistry

 I was pleased to speak at a webinar hosted by ViridisChem, where I spoke on ‘Managing Risk While Scaling Green Chemistry’. Follow the link to view the recording

Sustainable Technology Separations

Separations are the underappreciated workhorse of the refining, petrochemical and specialty chemicals industries.  This covers everything from crude towers used to separate crude oil into fractions for various refining technologies, filtration and centrifugation used to recover crystallized products in specialty chemicals, and distillation used for product recovery in most products that we use every day.

In 2016, Nature published an article titled ‘Seven chemical separations to change the world’. The seven identified included: 

1)      hydrocarbons from crude oil

2)      uranium from seawater

3)      alkenes from alkanes

4)      greenhouse gases from emissions

5)      rare-earth metals from ores

6)      benzene derivatives

7)      trace contaminants from water

This list covers both improvements to existing separations (1, 3, 6) and new separation needs for a more sustainable future (2, 4, 5, 7). Let’s look at three areas where advances in separation technology will be critical to get to a sustainable, low carbon future:

·       Separations to enable new chemical, biological, or electrochemical technology

·       The separation IS the novel technology

·       Energy efficiency—boring, but effective

Separations to enable novel reactor technology:    Separations are important for advancing sustainable technology because new technologies introduce new separation challenges, such as recovery of intracellular products from microbes, often referred to as Downstream Processing (DSP), separation and purification of bio-oil fractions produced from pyrolysis of waste plastic or biomass, or gas separations from electrolytic reduction of CO2. 

While these separation problems are new, the solutions will likely rely on conventional unit operations like membranes, solvents, solid sorbents, and distillation.

When developing a new technology, it is important to think about the separations from the start.  The figure below that shows that by integrating these steps early in technology development, we can develop a more optimized flow scheme. It is sometimes tempting to focus exclusively during early-stage R&D on maximizing yield or conversion in the reaction. This is a mistake. While it is important to understand the conditions needed to maximize yield or conversion, this is rarely the optimal overall condition. Perhaps high conversion generates more byproducts, reducing yield and increasing downstream separation costs.  Perhaps we need higher pressure or temperatures to achieve that high conversion, which may create additional downstream costs.  We can avoid surprises by integrating the separation system early in technology development and evaluating reaction/separation tradeoffs.

The Separation IS The Technology

Removal of CO2 from air seems crazy, and probably is. Any ‘smart’ engineer will tell you that trying to recover a gaseous component at a 400 ppm concentration from a stagnant gas at ambient pressure is a fool’s errand, but that’s what we are trying to do.  As a newbie at (pre-Honeywell) UOP we were fortunate to have a visit from Nobel Laureate George Olah, when he spoke about his ideas around a methanol economy which included CO2 recovery from the air. A few of us sat around afterward pooh-poohing the silly academic who thought this was a good idea, because there was no way this could be done economically.  Well, here we are nearly 25 years later, and a number of companies are trying to do just that, with billions of $ in investment flowing into this space.  I think we have no choice but to pursue this route, which in addition dramatically reducing the amount of CO2 that flows into the atmosphere in the first place.  A number of creative companies are exploring ways to do this with various solid sorbents or liquid solvents, innovating in ways that didn’t seem possible decades ago. 

Other interesting activities where the separation is the technology involve lithium recovery from brine or seawater for battery technology, and metal organic framework (MOFs) reaching maturity as advanced sorbents for a number of applications.

Energy Efficiency—Boring, but effective

Separations require energy input, usually using heat, pressure, or mechanical energy to drive the separation and often have a yield loss. So one way to improve sustainability of technology is to improve the separation and reduce energy demand of existing separations technology.  This can include:

·       Heat pumps/mechanical vapor recompression to improve the energy efficiency of distillation columns

·       Dividing wall distillation columns (DWCs) to replace multiple columns and reduce energy requirements

·       Membrane systems as pre/post separations to support existing distillation systems

Often, we are trading off additional capital to reduce operating cost, but also save energy and along with it reduce CO2 emissions associated with those energy sources. The growing availability of cheap, renewable electricity can open up new opportunities that may not have made sense in the past. 

Some guidelines when considering separations for sustainable technology:

·       Don’t forget about the separation.  Integration and optimization of the separation system with the reactor systems is important to achieving commercial success.

·       Don’t reinvent the wheel.  Often, we can use existing technology, perhaps with some modification, to accomplish the separation we need.  This is often a quicker and cheaper way to scale up.

·       Don’t forget about energy efficiency.  Typically low hanging fruit--may require some upfront capital with the benefits of lower operating costs and a significant reduction in CO2 emitting fuel sources.

Accelerate Scaleup While Managing Risk

We often hear that ‘if it was easy, anyone could do it’.  We can say something similar about the risk of new technology.  Developing a new technology is inherently filled with risk—if there was no risk, anyone would do it. While it may be possible to reduce risk to nearly zero, we also want to get to scale quickly, so I like to think about this in terms of managing risk while accelerating scaleup.  There are three key components to this approach

·       Assess Risk.  Before starting scaleup, a risk assessment should be completed, evaluating risk areas such as technical, commercial, supply chain, etc.  Scoring each risk by impact and probability provides an understanding of the most critical risks, so that these can be prioritized with mitigation steps outlined to support the scaleup effort.

·       Decouple scaleup parameters.  Next, it is important to decouple and prioritize the scaleup parameters.  This effort is informed by the risk assessment.  One mistake I see is trying to do everything at every scale.  The lab scale is best for evaluating the intrinsic parameters associated with the chemistry/biology of the system, while engineering parameters are better evaluated using calculations and modeling at early stages, with data generation at larger scales.  We can use this approach to develop a scaleup plan in which we define critical objectives for each stage—see below for a table outlining this approach.        

·       Develop multi-scale data in parallel.  Finally, multi-scale data is critical to any scaleup effort.   A very sequential approach to scaleup—aiming to hit commercial targets at each scale before moving to the next--can reduce risk significantly, but at the expense of timeline.  Timeline is a key driver for any new technology, so an alternate approach is to make strategic risk decisions and do much of this work in parallel—advancing to the next scale even while working on proving out commercial targets at the smaller scale.  With the risk assessment in place, and a clear scaleup plan, this risk can be managed while accelerating the timeline.  In addition, lab and pilot assets can be used for troubleshooting and continuous improvement during demonstration unit campaigns and while running first commercial plants—don’t shut down the lab or pilot just because the demo unit is up and running!   

Every organization must make their own evaluation about where they stand on the risk/timeline tradeoff.  By applying the principles introduced here informed decisions can be made to develop a risk-managed approach to scaleup.

Puzzled about scaleup? Multi-scale data is the key

Experimental data is[1] clearly the lifeblood of any new technology.  Getting data to prove out an invention can be the key to obtaining an important patent, generating early stage investment, and securing key partnerships.  Earlier postings established the links between experimental data and creative process engineering as well as robust, useful models.  However, generating data is expensive and time consuming, particularly as scale increases, making it critical to ensure that the right data is generated to make the best use of available resources. 

I like to start by looking at the scale-up effort as one integrated data gathering exercise, with the overall goal of generating the necessary data to define the commercial process design.  Along the way data is also needed to demonstrate a reduction in technical risk and allow optimization of the process economics.  This is a bit of a different mindset from trying to prove out a ‘result’ at each scale (e.g. proving conversion of raw materials A and B into product C with desired efficiency X in the lab, then the lab-pilot, then the pilot, and finally the demo).  So rather than charging ahead in result proving mode, some up front planning can ensure that the right data is gathered.   After all, all data are equal, but some are more equal than others (with apologies to George Orwell…) [2]

This planning effort will yield a scale-up plan with experiments designed to generate the necessary design data and identify the parameters that have the greatest impact on economics and technical risk.  In fact, the product of this effort is data, more than a physical fuel, chemical, or nutrition product. 

A key part of this early stage planning is decoupling these parameters, understanding that ‘science parameters’ such as reaction kinetics and separation factors can, and should, be explored at the lab stage.  Conversely, a lab scale effort to evaluate issues related to heat and mass transfer or pressure drop will be a futile effort at best leading to inconclusive or even incorrect results and is best done at a larger scale.  This decoupling is illustrated in the following table: 

Multi-scale data is beneficial for many additional reasons:

·       Model development. Data at multiple scales enables generation of robust models for process development and equipment design.  

·       Troubleshooting.  The smaller lab and pilot rigs can be instrumental to troubleshooting challenges in the larger units.  If possible, it is worth the investment in to keep these smaller units operating in support of the larger scale operations. 

·       Continuous improvement.  Continuous improvement is often needed while scaling a new technology to meet aggressive timelines and cost targets.  These improvements can be identified and scaled in parallel to ensure that the first commercial unit has the benefit of the learnings from several generations of technology improvements that are identified and de-risked in multiscale operations. 

By bringing Experimental Data together with Modeling and Analysis and Creative Process Engineering we develop a process concept, and an overall approach to reduce the time, cost, and risk of scale-up. 

[1] I used to make sure I strictly used ‘data’ as a plural noun as the OED intended, but decided a while ago that this is somewhat cumbersome, and perhaps even a bit pretentious.  I don’t think I am alone in this shift but am not sure the official definitions have caught up yet. 

[2] Original Quote: “All animals are equal, but some are more equal than others”, George Orwell, Animal Farm

Models Should Be Useful, Not Perfect!

The previous articles in this series presented ideas related to starting with a good Process Concept to drive the scale-up effort (‘Start with the Process Concept’), with Creative Process Engineering serving as one key aspect to this approach. 

We draw on Modeling and Analysis as a second key element: to set targets for economic and sustainability performance, encapsulate experimental data into engineering models, and design process equipment.  However, it is critical to recognize the limitations of models. British statistician George Box liked to say that all models are wrong, but some are useful[i].   For our purposes, models should be useful tools to support process development, scale-up, and design, rather than exact replications of the system in question.  To carry the analogy further, we need an entire toolbox at our disposal, and to make sure that we have the right tools for the right job. 

I typically like to start off with something simple and build out detail from there.  A simple mass balance using a spreadsheet is a great place to start!  We can then add additional detail to this simple model, and develop additional types of models depending on the requirements.  Examples of additional types of useful models include: 

·       Kinetic models for chemical and biological reaction systems.

·       Reactor design models for common reactor types, such as packed bed, trickle flow, fluidized bed, and external loop. 

·       Phase equilibrium models to support design of separation systems 

·       Life Cycle Analysis models for sustainability analysis. 

·       Technoeconomic models for economic analysis. 

·       Process simulation models for flowsheet and equipment design. 

The level of detail needed is driven by the requirements of the task at hand.

Where data does not exist, or is inconclusive, assumptions can be used to establish a working model.  We can then evaluate how critical those assumptions are to the system in question by exploring sensitivities.  If the answer is ‘very critical’, this result can be used to inform upcoming experimental activities.   This interplay between engineering design, modeling, and experimentation is quite important.  When modeling is done in a vacuum, with little or no interaction with experimentalists, the results is often a very beautiful model with limited value.  Similarly, some experimentalists insist it is impossible to model their system and find no value in the results that are spit out by an egghead running a spreadsheet.   The reality is that a useful model can, and should, complement experimentation to reduce the time and cost of scale-up, providing insight as to when additional data is needed to enhance understanding. A great model can also produce results and understanding that may be too time consuming, costly, or just not possible through additional experimentation. The models can also direct future opportunities for experimental programs. 

The models should then be refined as more data is collected—this is not ‘set and forget’.  This data should be generated at multiple scales to enhance the robustness and utility of the model.   The final article in this series will dive deeper into this critical issue of experimental data. 

[i] Box, G. E. P. (1979), "Robustness in the strategy of scientific model building", in Launer, R. L.; Wilkinson, G. N., Robustness in Statistics, Academic Press, pp. 201–236.

Creative Process Engineering

In my introductory article on this topic (Practical Technology Scaleup) I wrote about the benefit of drawing on Creative Process Engineering, Modeling & Analysis, and Experimental Data to develop a solid Process Concept to drive the scale-up effort--reducing risk and optimizing the economics of a sustainable technology.  

Creative Engineering, like Creative Accounting, may be an oxymoron or have negative connotations, but in my experience, it is critical for first of its kind technology.  Creative process engineers understand commercial plant design and can also deal with the ambiguity that is common with any new technology.  This creativity enables the engineers working closely with the science experts to develop the process concept, establish the material balance, and make key process design decisions to set the framework for the evolving novel technology.  These decisions fall into categories such as:

·       Product Requirements.  Product quality.  Waste vs Byproduct.  Batch vs Continuous.

·       Catalyst:  Composition.  Biological vs Thermochemical.  Size/shape.  Heterogenous vs Homogenous. 

·       Major Unit Operations.  Reactor concept.   Feedstock processing.  Separation processes.

·       Major Equipment.  Standard or Custom.  Pump/Exchanger/Compressor type.

·       Design Conditions.  Temperature.  Pressure.  Product Specifications. 

The challenge of translating discoveries from the lab into viable process flowsheets has been described by Douglas[i] to require assumptions 1) that fix parts of the process flowsheet 2) that fix some of the design variables and 3) that fix the connections to the environment.  Douglas estimates that more than one million process flowsheets can be generated just from the varied assumptions associated with the first process flowsheet.   Clearly it is not feasible to evaluate all of these alternatives. The good news is that we can just as quickly reduce the number of alternatives to a more manageable number but need good engineering judgement to make decisions with relatively little information.  This is where the Creative aspect of Process Engineering is critical. 

In practicality I find it is best to identify the reactor concept and separation scheme that are the best options, and then build the flowscheme around these.  Often, this is a case of screening out the ‘bad options’ resulting in several process concepts that make sense.  We can then define the data needed by Experimentation and Modeling & Analysis to refine our choices to the best option.  These areas will be explored in future articles. 

[i] Douglas, J.M. “A Hierarchical Decision Procedure for Process Synthesis”, AIChE Journal, March 1985, Vol. 31. No 3, pp 353-362. 

Practical Technology Scaleup

The Key to Launching Sustainable Technology

We are in the midst of a global crisis with the need to reduce carbon across all industries in order to limit global warming to 1.5 deg C above pre-industrial levels, as established in The Paris Agreement[1].  This drives a need for breakthrough technologies across all industries that can both reduce carbon and create value.  We can draw on past experience to reduce the time, cost and risk of technology scaleup, through some guidelines and practices that are the key to Practical Technology Scaleup.   This increases the chance of success for individual technologies and will enable us as a society to meet these aggressive climate targets. 

I have had the chance to scale-up and launch new products and technologies across a range of industries including sustainable fuels, renewable chemicals, bioprocessing, petrochemicals, specialty chemicals, distillation, and catalysis, and in my 23 years of industrial experience have developed a series of rules and guidelines to scaling and launching new technology.  The challenge with each has been to:

·       reduce technology risk

·       reduce time to market

·       reduce cost

·       maximize value

These are often competing objectives, and usually reducing time to market and reducing risk win out.  Of course, if the capital and operating cost are too high then we will not be successful, so we cannot ignore these criteria either.   

It is critical to ‘start with the end in mind’ using a Technology Concept (or Process Concept) that is used as a framework to drive new technology development, scale-up and commercialization.  This technology concept is not set in stone, and, in fact, should be reviewed and updated as we progress throughout the scale-up effort.  We establish the technology concept to drive the scale-up effort, not just inform it. This then enables us to direct the innovation to create the greatest value from breakthrough and disruptive ideas, as we identify challenges early, fail fast when it is cheaper and quicker, and make sure our efforts are focused on solving commercially relevant problems.

The technology concept is developed, and iteratively revised, through a combination of Creative Process Engineering, Multi-scale Experimental Data, and Modeling and Analysis. 

Creative Process Engineering:  The flow scheme is developed, the material balance is estimated, and key process design decisions are identified so that we can establish the best process flowsheet for the technology.  

Modeling & Analysis:  A good model can save time and $$ in the lab.  Coupled with the right analysis, this can be used to prioritize objectives in the lab, pilot, and demo units.   Cautionary note--useful models are more important than perfect models!

Experimental Data:  We need the right data to prove out breakthrough ideas, secure partners and investors, and develop engineering data for equipment design.  Mutli-scale data is critical to this effort, and with good planning, multiple assets and external resources can be leveraged.

The key benefits to this approach are:

•               Prioritization of R&D to de-risk and optimize a new technology.

•               Identification of cost reduction opportunities throughout the scale-up effort.

•               Anticipation of engineering needs as early as possible. 

In this way we can reduce risk and optimize economics of our new design, while efficiently managing the time and cost of our efforts.

In future posts I will elaborate on the key concepts presented in this introduction. 

[1] https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement

2018 AIChE Process Development Symposium (PDS)

I attended the 2018 PDS last week in the outskirts of Chicago.  This event is always a great forum to share the latest findings and best practices in process development across industries.  I saw a number of common themes emerging from the talks and posters (see the technical program here).  

• Look inside your organization.  Know who is doing something that may help you solve your problem.

• Look outside your organization.  Is someone else working in an area that could help your project?  Could be a great partnering opportunity!

• Use data driven gate reviews, and make sure to have a systemic scale-up strategy, rather than a random walk to find results.  

• Communication is critical.  Make sure the key internal and external stakeholders understand the value of your process development activities.  

• Sustainability targets are real in many organizations, and driving process development objectives.  

• Persistence and patience is important.  It takes time to work through scale-up!  

• And most importantly, invent and innovate, but do things that matter and can get to market.  I learned early in my career that there is no shortage of technical problems to solve, so better to focus on things that can have a sustainable and economic impact.

  Thanks to AIChE for putting on a great event!

Scaling New Technology

The project development cycle for an established process technology is well known, with an initial Conceptual Design phase to define the project, develop a block flow diagram, and generate a cost estimate that is typically +/- 50%. Feasibility, Basic Engineering, Detailed Engineering, Procurement and Construction, and Start-up then follow. 

The conceptual design phase for an established technology can generally be completed in 2-4 months. However, for a new technology we need much more time to get this right! To do this we can bring process engineering into the picture as early as possible, even before discovery R&D. In fact, if we start with the conceptual design, or process concept, we can use this as a framework to drive new technology development, scale-up and commercialization. This process concept is not set in stone, and, in fact, should be reviewed and updated as we progress throughout the scale-up effort. 

See the rest of the article published here.