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Our aproach
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Data logistics
Data logistics
Inventory of data sources, public and private
Inventory of variables and construction of the lineage of each output variable
Design and implementation of production grade Acquisition, Transformation and Storage Pipelines
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Models
Models
Iterative design and implementation of each model, increasing its accuracy category from “proxy” to “high”. (For almost all models there is an initial reference version, although they must be validated by an SME)
Seamless integration of OSS / Commercial Optimization & AI Tooling / Commercial Big Data Computing (Snowflake, Databricks, BigQuery, etc.
Open Data architecture for data storage and query
Creation of a high-resolution georeferenced index (H3) to store all precomputed variables
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From the very beginning...
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Input
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Process
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Output
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POC Proposal
Scope & Assumtions
The scope is on feedstocks highly compatible with biogas production, specifically for anerobic digestion
Spain as region of focus (high quality data, major biogas producers)
Assume that the user interface will be an existing tool that provides the minimum functionalities required to analyze the outputs (PowerBI, Tableau, Carto, etc.)
Assume that minimizing data and infrastructure costs is a project priority
DKL will provide 2 full-time resources for 3 months. The resource profile will vary depending on the project phase between Data Architect, Data Engineer, Backend Developer, Data Scientist & Project Manager
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A
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Numbers don't lie
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567
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Model Suite
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Operations
Agricultural Yield Predictor
ML Biogas Production Optimizer
Biomass Logistics Optimizer (feedstock procurement)
Locations
Biogas / Biomethane Facility Location Optimizer
Biogas / Biomethane Facility Locations Evaluator
Demand
BioEnery
Demand Analysis
Supply
Manure Availability for Production
Organic Waste Availability for Production
Agricultural Residues Availability for Production
Multidimensional Supply Cost Analysis
Localized Feedstock Stress Analysis
Feedstock Potential Analysis