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.