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
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!