This hackathons is only open to students. Double check the event page for more information as this may mean only those from a particular university/country are eligible.
Event Type
online
126
Participants
₹15,000
Prize Pool
11
Est. Projects
Organizers
Alex Johnson
alex@example.org
Jamie Rivera
jamie@example.org
Optimiser – AZeotropyy’26 Overview: Optimiser is a simulation-based optimization competition where participants must determine the optimal values of decision variables C and R that minimize the total cost T using the provided Optimiser Simulator App. Teams query values of C and R; the simulator returns the corresponding T. The challenge is to strategically select data points within a limited query budget to build an accurate surrogate model and identify the global minimum efficiently. Simulator link and setup instructions will be shared via email and the official WhatsApp group (expected by 28th February). REGISTRATION DEADLINE :- 28th feb 2026 SUBMISSION DEADLINE :- 4th march 2026 WhatApp group link :- https://chat.whatsapp.com/L1kPhXfa60NBU7IGtRH2fS full details of the competition :- https://drive.google.com/drive/folders/1FUcJ5rxsZGk4rOkw2Bsf_VEp9-YC5-8b Competition Structure: Participants will: Use the Optimiser Simulator App to query values of C and R. Obtain the corresponding output T. Strategically use a maximum of 20 simulator runs. Build a surrogate model using mathematical/statistical tools. Predict the optimal values of C and R to minimize T. Submit a combined final report and code file. Note: Each simulator run is considered expensive; intelligent query selection is critical. Submission Requirements: Each team must submit One combined submission: Final Report (PDF) Code / Notebook File Permitted Tools: Python, MATLAB, Excel, or equivalent software Rules: Only one team member should submit the final solution. Multiple submissions are prohibited and will lead to disqualification. Submission must include simulator query logs. Report Format & Guidelines: The report must include: Team Details: Team ID Names of all members Contact details Experimental Data: Table of all queried data points Corresponding values of C, R, and T Methodology: Data selection strategy Exploration and refinement approach Noise handling (if applicable) Modelling & Analysis: Surrogate model used Mathematical formulation & relevant equations Plots, tables, calculations Description of optimization method Final Results: Predicted optimal values of C and R Corresponding minimum value of T Formatting Requirements: Well-structured PDF Numbered figures and tables with captions Clear written explanations with mathematical rigor Evaluation Criteria: Data Strategy – 30 Marks Intelligent selection of simulator runs Effective domain exploration Efficient refinement of search Efficient use of query budget Query Efficiency Bonus: Max 20 runs allowed Bonus = (20 – X) × 1 (X = simulator runs used) Maximum bonus: 10 marks (≤10 runs) Technical Analysis – 45 Marks Appropriate mathematical/statistical tools Quality of surrogate modelling Correctness of methodology Effectiveness of optimization approach Accuracy of Final Solution – 20 Marks Within 1% → 20 marks Within 3% → 15 marks Within 5% → 10 marks Within 10% → 5 marks Report Quality – 5 Marks Clarity and organization Quality of plots and tables Mathematical rigor Overall presentation Eligibility Criteria: Maximum 2 participants per team Open to undergraduate and postgraduate students Cross-institution teams allowed At least one member must be from Chemical Engineering Participants can register for only one team Selected team must come to IIT Bombay on 14th March 2026 for verification, certificates, and prizes Registration & Submission Process: Fill out the registration form by 28th February. Submit the final submission form by 4th March with: Team name Final Report (PDF) Code/Notebook file All findings and query logs Certificates & Prizes: Top 5 teams: Cash prizes totaling INR 15,000 Top 10 teams: Certificate of Appreciation All valid submissions: Certificate of Participation Kindly go through the pdf provided in the additional info.