All posts by NYU Tandon School of Engineering

NYU Spearheads Project to Help Chemical Industry Go Green

Post Syndicated from NYU Tandon School of Engineering original https://spectrum.ieee.org/green-tech/conservation/nyu-spearheads-project-to-help-chemical-industry-go-green

A team at New York University’s Tandon School of Engineering  is playing a key role in forging a collaboration involving over a dozen US universities and national laboratories aimed at sparking — literally — a fundamental change in how the US chemical industry operates.

The goal is to address the most daunting task looming over the industry: how to make industrial chemistry — especially petrochemistry — greener and more sustainable, partly to meet the escalating demands of greenhouse emission regulations. The nascent, multi-institutional effort will be called “Decarbonizing Chemical Manufacturing Using Sustainable Electrification,” or DC-MUSE.

DC-MUSE was conceived this summer in a workshop attended by over 40 companies and institutions, and organized by a planning grant from the National Science Foundation to build capacity in convergent research. Its aim is to develop technologies and strategies to help the US chemical industry migrate from thermal-based manufacturing processes to electricity-based ones.

A range of government regulations aimed at achieving zero-carbon emissions are driving this migration. These greenhouse emissions regulations will progressively come into effect in the coming decades, culminating, for example, in the European Union’s aim to reduce 95 percent of 1990 level greenhouse emissions by 2050. These and other international regulations on greenhouse emissions could threaten up to 12 percent of all US exports ($220 billion),  if the US chemical industry is not able to decarbonize its processes. The task is clearly enormous, not just for the industry itself but for the larger economy.

“Thirty percent of US industrial CO2 emissions comes from the chemical industry, and 93% of the chemical processes use fossil fuel heat,” noted Andre Taylor,  associate professor at the NYU Tandon School of Engineering. “We’re talking about changing a whole industry that also involves a huge societal impact, encompassing 70,000 products, and 25% of the US gross domestic product.”

Many experts believe that the first step in overhauling the chemical industry will involve moving away from thermally-driven chemical reactions and separation processes that require heat from fossil fuels and moving towards reactions that use electricity generated by renewable resources, like wind and solar.

While this migration has already started to occur, with penetration of renewable sources into the US electrical grid doubling in the past decade, the technologies for integrating these sources into cost-effective electrified chemical processes has remained practically non-existent.

“After meeting with many chemical industry representatives, we learned that technologies that would enable electrification on the industrial scale don’t exist at this time,” said Yury Dvorkin, assistant professor at NYU’s Tandon School of Engineering. “The industry needs support to develop these technologies so they can be adopted in a way that’s economically feasible.”

One of the areas that Dvorkin and his colleagues believed they needed to focus on was overcoming emerging reliability issues that inhibit and increase the cost of using renewable energy in the electrical grid. In other words, how do you ensure that there are no supply interruptions to the delivery of electricity when energy from the sun and wind can be intermittent?

At the moment, energy storage technologies are not entirely up to the task of balancing out the intermittency of renewable electricity. As a result, NYU Tandon researchers have been looking at storing energy in the form of chemical bonds, as opposed to electrons, as a possible solution.

In energy storage approaches like this, energy is stored chemically in the form of hydrogen, and that hydrogen is reused later in a fuel cell. The fuel cells used to capture the energy are referred to as redox-flow batteries (RFBs). RFBs consist of a positive and negative electrolyte stored in two separate tanks. When the liquids are pumped into the battery cell stack situated between the tanks, a redox reaction occurs and generates electricity at the battery’s electrodes.

Several NYU researchers recently published a paper in the journal Cell Reports Physical Science that looks at improving the energy storage capabilities and economics of these RFBs.

The NYU researchers didn’t simply tweak RFB technology to improve its energy density or reduce their costs. Instead of just plugging RFBs into renewable energy sources to store their intermittent energy production, the NYU researchers demonstrated how you could use RFB concepts to completely integrate chemical manufacturing into the whole energy storage process.

“In principle, you can imagine chemical plants acting as energy storage reservoirs, but at the same time producing chemical products,” explained Miguel Modestino,  an assistant professor at NYU, and one of the co-authors of the Cell Reports paper. “The storage value it provides lowers the cost for the production of the chemical that you want to make at the end of the day.”

Modestino added that this approach also allows the chemical companies to integrate fluctuating sources of electricity, like renewables. You can thus decarbonize the industry in a way that is both economic and functions well with the dynamics of a renewable-driven grid.

The DC-MUSE project has expanded dramatically since its ideas first took root a few months ago. The project has already put together a group of 30 investigators from 11 universities and 3 National Laboratories that cover a wide spectrum of research areas.

 At NYU Tandon, Ryan Hartman, associate professor, is leading a group to develop plasma catalysis technology for these types of chemical reactions. Taylor’s and Modestino’s groups are working on electrochemical reactors for chemical manufacturing. And Dvorkin has been working on integrating these plants within the grid. Other groups outside of NYU are investigating using membranes for separations and system integration.

In addition, the NYU team has been consulting with faculty at the law school and the business school on how to design policies that can enable the economic transition towards renewable energy-driven chemical manufacturing.

The researchers are also reaching out to industry to get early involvement. In fact, the genesis of the DC-MUSE project was a workshop in which NYU invited 50 industry experts and people from academia to come together to talk about the challenges in the chemical industry, such as process intensification.

“We have been talking with people in the big chemical manufacturing companies, who have started to develop pilots for electrified chemical production,” said Elizabeth Biddinger, City College of New York. Biddinger and Modestino recently published an article in ECS Interfaces describing how environmental advantages of electro-organic syntheses such as minimizing waste generation, utilizing non-fossil feedstocks, and on-demand chemical manufacturing are also large drivers for sustainability in chemical processes across multiple sectors.

The involvement of petrochemical companies is not by accident.  Petrochemical processes—and actually a very small subset of petrochemical processes—account for more than 80 percent of the energy and CO2 emissions from chemical processes, according to Modestino.

As the DC-MUSE picks up momentum, its architects at NYU envision the project as a go-to Center for the fundamental engineering research that is needed to enable these technologies. Said Modestino, “The way that we see it is that you do the research in the lab, you develop with lab-scale demonstrations, but then through partnerships with the companies you’ll develop them into processes.”

While the DC-MUSE project awaits its expanded aim though increased funding, it is already having an impact on the pedagogical approach of the NYU professors.
 

“We already have had discussions about joint Ph.D. positions so that a student can have multiple advisors,” said Dvorkin. “In this way, we can really work together on these problems and provide students with a multidisciplinary perspective, because without this sort of collaboration, without this input delivered to the students, there is no way to solve societal problems.”

Taylor added: “From the applications we’ve seen into our program, we know that people want to pursue things that actually have an impact on changing society and improving the world. People want to discover something fundamental, but if it has a broader societal impact, people can see its importance. This is why I do research in this area.”

To learn more about initiatives that are going on at NYU’s Tandon School of Engineering, please visit its website.  

The Devil is in the Data: Overhauling the Educational Approach to AI’s Ethical Challenge

Post Syndicated from NYU Tandon School of Engineering original https://spectrum.ieee.org/computing/software/the-devil-is-in-the-data-overhauling-the-educational-approach-to-ais-ethical-challenge

The evolution and wider use of artificial intelligence (AI) in our society is creating an ethical crisis in computer science like nothing the field has ever faced before. 

“This crisis is in large part the product of our misplaced trust in AI in which we hope that whatever technology we denote by this term will solve the kinds of societal problems that an engineering artifact simply cannot solve,” says Julia Stoyanovich,  an Assistant Professor in the Department of Computer Science and Engineering at the NYU Tandon School of Engineering, and the Center for Data Science at New York University. “These problems require human discretion and judgement, and where a human must be held accountable for any mistakes.”

Stoyanovich believes the strikingly good performance of machine learning (ML) algorithms on tasks ranging from game playing, to perception, to medical diagnosis, and the fact that it is often hard to understand why these algorithms do so well and why they sometimes fail, is surely part of the issue. But Stoyanovich is concerned that it is also true that simple rule-based algorithms such as score-based rankers — that compute a score for each job applicant, sort applicants on their score, and then suggest to interview the top-scoring three — can have discriminatory results. “The devil is in the data,” says Stoyanovich.

As an illustration of this point, in a comic book that Stoyanovich produced with Falaah Arif Khan entitled “Mirror, Mirror”,  it is made clear that when we ask AI to move beyond games, like chess or Go, in which the rules are the same irrespective of a player’s gender, race, or disability status, and look for it to perform tasks that allocated resources or predict social outcomes, such as deciding who gets a job or a loan, or which sidewalks in a city should be fixed first, we quickly discover that embedded in the data are social, political and cultural biases that distort results.

In addition to societal bias in the data, technical systems can introduce additional skew as a result of their design or operation. Stoyanovich explains that if, for example, a job application form has two options for sex, ‘male’ and ‘female,’ a female applicant may choose to leave this field blank for fear of discrimination. An applicant who identifies as non-binary will also probably leave the field blank. But if the system works under the assumption that sex is binary and post-processes the data, then the missing values will be filled in. The most common method for this is to set the field to the value that occurs most frequently in the data, which will likely be ‘male’. This introduces systematic skew in the data distribution, and will make errors more likely for these individuals.

This example illustrates that technical bias can arise from an incomplete or incorrect choice of data representation. “It’s been documented that data quality issues often disproportionately affect members of historically disadvantaged groups, and we risk compounding technical bias due to data representation with pre-existing societal bias for such groups,” adds Stoyanovich.

This raises a host of questions, according to Stoyanovich, such as: How do we identify ethical issues in our technical systems? What types of “bias bugs” can be resolved with the help of technology? And what are some cases where a technical solution simply won’t do? As challenging as these questions are, Stoyanovich maintains we must find a way to reflect them in how we teach computer science and data science to the next generation of practitioners.

“Virtually all of the departments or centers at Tandon do research and collaborations involving AI in some way, whether artificial neural networks, various other kinds of machine learning, computer vision and other sensors, data modeling, AI-driven hardware, etc.,” says Jelena Kovačević, Dean of the NYU Tandon School of Engineering. “As we rely more and more on AI in everyday life, our curricula are embracing not only the stunning possibilities in technology, but the serious responsibilities and social consequences of its applications.”

Stoyanovich quickly realized as she looked at this issue as a pedagogical problem that professors who were teaching the ethics courses for computer science students were not computer scientists themselves, but instead came from humanities backgrounds. There were also very few people who had expertise in both computer science and the humanities, a fact that is exacerbated by the “publish or perish” motto that keeps professors siloed in their own areas of expertise.

“While it is important to incentivize technical students to do more writing and critical thinking, we should also keep in mind that computer scientists are engineers.  We want to take conceptual ideas and build them into systems,” says Stoyanovich.  “Thoughtfully, carefully, and responsibly, but build we must!”

But if computer scientists need to take on this educational responsibility, Stoyanovich believes that they will have to come to terms with the reality that computer science is in fact limited by the constraints of the real world, like any other engineering discipline.

“My generation of computer scientists was always led to think that we were only limited by the speed of light. Whatever we can imagine, we can create,” she explains. “These days we are coming to better understand how what we do impacts society and we have to impart that understanding to our students.”

Kovačević echoes this cultural shift in how we must start to approach the teaching of AI. Kovačević notes that computer science education at the collegiate level typically keeps the tiller set on skill development, and exploration of the technological scope of computer science — and a unspoken cultural norm in the field that since anything is possible, anything is acceptable.  “While exploration is critical, awareness of consequences must be, as well,” she adds.

Once the first hurdle of understanding that computer science has restraints in the real world is met, Stoyanovich argues that we will next have to confront the specious idea that AI is the tool that will lead humanity into some kind of utopia.

“We need to better understand that whatever an AI program tells us is not true by default,” says Stoyanovich. “Companies claim they are fixing bias in the data they present into these AI programs, but it’s not that easy to fix thousands of years of injustice embedded in this data.”

In order to include these fundamentally different approaches to AI and how it is taught, Stoyanovich has created a new course at NYU Tandon entitled Responsible Data Science. This course has now become a requirement for students getting a BA degree in data science at NYU. Later, she would like to see the course become a requirement for graduate degrees as well. In the course, students are taught both “what we can do with data” and, at the same time, “what we shouldn’t do.”

Stoyanovich has also found it exciting to engage students in conversations surrounding AI regulation.  “Right now, for computer science students there are a lot of opportunities to engage with policy makers on these issues and to get involved in some really interesting research,” says Stoyanovich. “It’s becoming clear that the pathway to seeing results in this area is not limited to engaging industry but also extends to working with policy makers, who will appreciate your input.”

In these efforts towards engagement, Stoyanovich and NYU are establishing the Center for Responsible AI, to which IEEE-USA offered its full support last year. One of the projects the Center for Responsible AI is currently engaged in is a new law in New York City to amend its administrative code in relation to the sale of automated employment decision tools.

“It is important to emphasize that the purpose of the Center for Responsible AI is to serve as more than a colloquium for critical analysis of AI and its interface with society, but as an active change agent,” says Kovačević. “What that means for pedagogy is that we teach students to think not just about their skill sets, but their roles in shaping how artificial intelligence amplifies human nature, and that may include bias.”

Stoyanovich notes: “I encourage the students taking Responsible Data Science to go to the hearings of the NYC Committee on Technology.  This keeps the students more engaged with the material, and also gives them a chance to offer their technical expertise.”