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SUMMARY:Accelerating the Transition to Carbon Neutrality
DESCRIPTION:ChE Seminar Series Presents: \nMadga Barecka\, Ph.D. \nPost-Doc at University of Cambridge\, Research Centre in Singapore \nAbstract \nTransition to Net Zero 2050 requires immediate and drastic changes in the current manufacturing methods. This transformation is difficult to realize without disrupting the existing industries and putting at risk the delivery of the products that our society relies on. To address this challenge\, I proposed an alternative approach: use of novel\, carbon-neutral technologies such as CO2 electrolysis as a retrofit\, which operates in parallel to an existing chemical plant\, can be installed with a minimum disruption to the ongoing manufacturing activities and leads to a meaningful reduction of the carbon footprint. This technology\, Carbon Capture On-site Recycling\, will be illustrated with examples of several chemical manufacturing processes\, where\, if fully deployed\, it could allow to save annually up to 10 Gt of CO2 emissions by 2050. \nThis work is a part of my broader vision on disrupting the global carbon cycle through both discovery and scaling of circular production methods for chemical\, pharmaceutical and environmental sectors. How to encourage the industry to change and adopt innovative technologies? How to functionally reproduce photosynthesis to deliver carbon neutral chemicals? How to improve the access to medicines for those most exposed to distribution injustice? In my talk\, I will discuss my current and future research that will significantly contribute to answering these questions. \nBio \nDr. Magda H. Barecka is a Post-Doc at University of Cambridge\, Research Centre in Singapore. She is interested in accelerating the adoption of CO2 conversion\, powered by renewable energy\, and the development of economically viable and scalable carbon neutral production methods. Dr. Barecka holds a PhD degree from TU Dortmund University (Germany) and was the first PhD candidate to be awarded the title as a Double Diploma certificated together with Lodz University Technology (Poland). She is a chemical engineer with expertise in process intensification\, retrofitting and design\, developed in academia and private sector. As a part of her PhD thesis\, she developed a methodology supporting implementation of intensified technologies in the chemical manufacturing\, which was transferred to Industry (Processium company\, France/Brazil). After the completion of her PhD\, she joined pharmaceutical/fine chemicals sector in Switzerland and worked on the design of manufacturing lines\, as well as established collaborations with Academia towards the development of algorithms accelerating process development. After this\, she came back to the research sector to deploy her process design experience in the field of carbon capture and utilization. Dr. Barecka is currently working in the intersection of CO2 electrolysis process design\, reaction optimization\, integration with renewable energy sources\, and techno-economic analysis for CO2-based manufacturing methods that can disrupt the carbon cycle. \nPlease contact a.ramsey@northeastern.edu for the remote seminar link.
URL:https://coe.northeastern.edu/event/accelerating-the-transition-to-carbon-neutrality/
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SUMMARY:ECE PhD Proposal Review: Yuanyuan Li
DESCRIPTION:PhD Proposal Review: Submodularity in Cache Networks \nYuanyuan Li \nLocation: Zoom Link \nAbstract: As information-based demand surges\, distributed network services\, e.g.\, cache networks\, play an important role to mitigate network traffic. Cache networks are a natural abstraction for many applications\, including information-centric networks\, content delivery networks\, cloud computing\, and edge/wireless IoT. How to allocate resources (routing\, placing items in caches\, flow control\, etc.) in cache networks is a crucial problem\, as resources (storage space\, and bandwidths) are usually limited. Resource allocation in networks has been traditionally approached through classic convex optimization. However\, simple problems becomes combinotorial in cache networks\, which leads to NP-hardness. Enlightened by several works studying cache networks\, we identify a useful property\, submodularity\, which is the key to approximation algorithms solving those NP hard resource allocation problem in cache networks.\nLeveraging submodularity\, we study a cache network\, in which intermediate nodes equipped with caches can serve content requests\, from different angles.\nFirst\, we model this network as a universally stable queuing system\, in which packets carrying identical responses are consolidated before being forwarded downstream. We refer to resulting queues as M/M/1c or counting queues\, as consolidated packets carry a counter indicating the packet’s multiplicity. Cache networks comprising such queues are hard to analyze; we propose two approximations: one via M/M/∞ queues\, and one based on M/M/1c queues under the assumption of Poisson arrivals. We show that\, in both cases\, the problem of jointly determining (a) content placements and (b) service rates admits a poly-time\, 1-1/e approximation algorithm. We also show that our analysis\, with respect to both algorithms and associated guarantees\, extends to (a) counting queues over items\, rather than responses\, as well as to (b) queuing at nodes and edges\, as opposed to just edges.\nSecond\, we refer to the cost reduction enabled by caching as the caching gain\, and the product of the caching gain of a content request and its request rate as caching gain rate. We aim to study \emph{fair} content allocation strategies through a utility-driven framework\, where each request achieves a utility of its caching gain rate\, and consider a family of α-fair utility functions to capture different degrees of fairness. The resulting problem is an NP-hard problem with a non-decreasing submodular objective function. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to 1-1/e. When 0 < α ≤ 1\, we further propose a randomized strategy that attains an improved optimality guarantee\, (1-1/e)^(1-α)\, in expectation.\nThird\, We study a cache network under arbitrary adversarial request arrivals. We propose a distributed online policy based on the online tabular greedy algorithm. Our distributed policy achieves sublinear (1-1/e)-regret\, also in the case when update costs cannot be neglected.\nFinally\, we propose an experimental design network paradigm\, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners\, and the decision variables are the data transmission strategies subject to network constraints. We first show that\, assuming Poisson data streams\, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a 1-1/e factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-yuanyuan-li/
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