Optimizing Brewing Parameters for Quality Beer
Client Overview
The client represents one of the world’s largest brewing companies, owning several breweries. Their portfolio includes over 30 international, national, and regional beer brands.
Project Objective
The project aimed to achieve target parameters for beer wort color and pH. This was accomplished through data analysis and the development of a recommendation system. The system predicts optimal dosages of specialty malts and orthophosphoric acid.
Project Scope
The project involved the following steps:
Data Extraction:
We collected brewing process data from 2019 and 2020.
Data Preparation:
Cleaning and preprocessing of the data.
Model Training:
Building predictive models using machine learning techniques.
Recommendation System
As part of the implementation, a recommendation system was developed for dosing recipe components. The system is accessible via a user-friendly web interface hosted on Microsoft Azure Web App. Brewers input data related to malt quality and brewing parameters, receiving recommendations for adjusting recipe components.
Results
The project achieved the following:
Automated Data Collection:
A strategy for automating data collection and extraction.
Dosing Model:
Creation of a model recommending optimal dosages of specialty malts and orthophosphoric acid.
Quality Improvement Recommendations:
Actionable suggestions to enhance the model’s quality, initially not evident during the project’s early stages.
The insights gained will drive further optimization of the brewing process, ensuring high-quality beer production while maintaining efficiency and consistency.
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