Shell.ai hackathon for sustainable and affordable energy
Shell.ai Hackathon for Sustainable and Affordable Energy brings together brilliant minds passionate about digital solutions and AI, to tackle real energy challenges and help build a lower-carbon world where everyone can access and afford energy.
Shell.ai Hackathon 2023: Agricultural Waste Challenge
Meet the winners of Shell.ai Hackathon for Sustainable and Affordable Energy: Agricultural Waste Challenge
This year鈥檚 edition of Shell.ai Hackathon was nothing short of extraordinary. We hit the participation record with 5,762 registrations and 9,400 submissions, which is an astounding response to a complex challenge the participants faced this year: optimising a supply chain of agricultural waste collection. For the first time in the hackathon鈥檚 history, top finalists from different parts of the world came to pitch their solutions live on stage during the Changemakers of Tomorrow at the Shell Technology Centre in Bangalore. They meet face-to-face with Shell.AI leaders and celebrated their success together with the Shell.ai Hackathon team.
Each team presented a unique vision, creative approach to the problem, and a true passion for accelerating a future where digital solutions tackle complex problems in the energy sector. The winners of the University Edition will further explore the opportunity of an R&D project with Shell.
Team Midas
University Edition winner
Team Members:
Wei Ping Lam, PHD Student of Chemical Engineering at Rice University, Houston, Texas, USA
Wei Ping about his solution: 鈥淚 reformulated a multi-faceted supply-chain optimisation problem into a single-objective one. Looking at the data I realised the transportation costs of residual biomass are much less than its under-utilisation costs, which implied the biorefineries and depots must operate at maximum capacity, with zero under-utilisation costs, further reduced by placing depots close to refineries. In the commercialisation phase, I took advantage of remote sensing data (satellite data on rainfall and agricultural production for a region). This method combined high-performance computing gave amazing results. I also used an idea of a collaboration platform where experts help scale and deploy the solution.鈥�
Team Perception
General Edition winner
Team Members:
Parth Singhal, Senior Analyst at Goldman Sachs
Sakshi Tyagi, Senior Data Scientist at Siemens Advanta
Team Perception about their solution: 鈥淲e managed to solve a complex problem with an easy solution by leveraging open-source data and cutting-edge technology. An analysis of different open-source data (from the government of India and the state of Gujarat) was our starting point. Then, we integrated it to our model to predict the biomass forecast. We also made our highly optimised and customisable clustering algorithm from scratch! For us, the solution is not only about data and the algorithm, but about creating a tangible impact.鈥�
Team Shello There
University Edition Runner up
Team Members:
Vibin Mathiparambil Vinod, EEE student at Nanyang Technological University in Singapore
Vibin about his solution:
鈥淚 found the optimisation part of the problem statement particularly inspiring. What鈥檚 innovative about my solution is the use of a simple statistical model and external data on rainfall and altitude. My strategy for commercialising the product is creating a software or a web app where clients could upload the data and set their own parameters.鈥�
Team Optima
General Edition runner up
Team Members:
Sagnik Das
Arun Raman
Team Optima about their solution: 鈥淲e worked under the assumption of a future when biomass is produced on a larger scale than food, with individual households taking part in the biomass generation. This vision also includes irregular harvest cycles as well as biomass being traded as stock. Households sharing the volume of their own produce was key to optimising the supply chain. Our solution is customizable to different computational infrastructures, making it widely accessible.鈥�
Team <45xTrlop!*
General Edition 2nd runner up
Team Members:
M Barak Ouro-Akondo, Data Analyst and Supply Chain Management student at HESTIM Engineering and Business School, Casablanca, Morocco
Jacintho Mpeteye, Python Developer and Industrial Engineering student at HESTIM Engineering and Business School
Japhet Ayassou, Industrial Engineer, Junior Consultant in Innovation Funding at Leyton
Team <45xTrlop!* about their solution: 鈥� We focused on efficiency, sustainability and reliability. To overcome the limitations of our hardware, we adopted a divide-and-conquer strategy and broke down the problem into manageable pieces. What鈥檚 unique in our approach is that the batches aren鈥檛 isolated and the data feedback guarantees the constraints are satisfied at a global level. Achieving efficiency is also about doing the best with what you have 鈥� this is what we did!鈥�
