CASE STUDY  ·  RENEWABLE NATURAL GAS  ·  RFS COMPLIANCE Reclaiming the Month-End: How Morrow Renewables Eliminated Days of Manual Data Work and Accelerated RIN Monetization This case-study covers how a landfill-to-RNG operator leverages AI automation in monitoring, unifying, and assembling data to support its RFS compliance process. CUSTOMER Morrow Renewables CHALLENGE Manual Data Processing & Lengthy Process for RIN Generation OUTCOME 80% Reduction in Compliance Workload

80% Reduction in monthly compliance workload

< 1 min Data retrieval per site (was up to 2 full days)

+5 days Reclaimed per month for faster EMTS submission

THE CHALLENGE

When Compliance Runs on Spreadsheets, Month-End Becomes a Crisis

In the Renewable Fuel Standard (RFS) program, every Renewable Identification Number represents a discrete unit of verified compliance — and the verification process is only as reliable as the operational data that underpins it. For landfill-to-RNG operators, that data flows from continuous process measurements taken at the wellhead, through the treatment and compression facility, and into the gas delivery system. Any break in that data chain — a missing minute, an unresolved sensor gap, a unit conversion error — creates a vulnerability that can surface as an invalid credit, a delayed submission, or an adverse QAP audit finding.

Prior to Rimba, compliance reporting at Morrow Renewables relied on a thorough, hands-on workflow designed to ensure accuracy across multiple landfill-to-RNG facilities. Teams collected operational data from several independent systems (e.g. SCADA/data historian) and carefully assembled complete monthly datasets to support regulatory reporting requirements.

As the company expanded across multiple sites, the reporting process naturally became more complex. Large volumes of operational data had to be reviewed, organized, and validated before analysis could begin. Because the workflow depended on information from multiple systems, identifying missing or incomplete records often occurred later in the reporting cycle, making investigation and reconciliation more time-consuming.

Secondary backup systems also required additional preparation before the data could be incorporated into reporting files. Records frequently needed to be reformatted, organized chronologically, and normalized to align with the primary dataset. Any missing intervals had to be reviewed and reconciled manually before the final analysis files could be completed.

Despite the diligence of the process, the downstream reporting timeline remained lengthy due to the number of manual preparation and review steps involved. After internal validation, files were submitted for external review prior to final regulatory submission. As reporting demands increased alongside company growth, it became clear that a more streamlined and scalable solution was needed to support faster reporting cycles, improved visibility, lower compliance risk, and greater operational efficiency, leading to faster RIN generation.

THE SOLUTION

Rimba AI: Automated Ingestion, Real-Time Validation, and Protocol-Aligned RIN Preparation

Unlike horizontal data platforms or general-purpose BI tools, Rimba is designed around the specific data standards, substitution methodologies, and reporting requirements of the RFS program.

“Rimba’s AI-driven tools have improved our operational efficiency, increased our confidence in reporting and analytics, and meaningfully reduced the potential for human error. Rimba has quickly become a critical part of how we manage and trust our data.” Justin Kennard   —   Director of Development & Compliance, Morrow Renewables

Direct Integration with Existing Data Infrastructure

Rimba connects natively to Aveva Connect historian — the same infrastructure Morrow Renewables was already operating. There is no data migration, no parallel system to maintain, and no change to the underlying operational data architecture. The platform reads from the historian in automatically, applying the same data pulls that previously required manual initiation and monitoring, and surfaces the results through a structured compliance interface rather than raw exports.

For the data assembly workflow, this integration eliminated the manual reconciliation cycle entirely. Site data that previously required a full working day to pull, format, and validate now becomes available in under one minute. The unit conversions, chronological sorting, and gap identification steps that had consumed hours of staff time per data chunk are applied automatically.

Automated Gap Detection and Protocol-Compliant Substitution

Under RFS protocols, missing data is not simply an administrative inconvenience — it is a compliance event that requires a documented, methodology-compliant response. Rimba automates this response. When a gap is detected in the primary data stream, the platform identifies the applicable substitution method under the relevant protocol, applies it, and flags the substitution in the record so that auditors can verify the methodology. Where secondary data sources are required to fill the gap, Rimba handles the import, formatting, and integration automatically.

Report Generation and EMTS Preparation in Minutes

With data assembly automated, the biogas token generation and RIN generation are executed by Rimba in a structured, protocol-aligned sequence. The platform performs the same checks and generates the files required for EMTS upload in a fraction of the time previously required. The output is the same exact format to the RFS templates that can be found at the EPA website.

BUSINESS IMPACT

Faster Credits, Lower Risk, and a Compliance Function Built to Scale

Time Recaptured and Monetization Accelerated

The most immediate impact of Rimba's deployment was the compression of the monthly compliance cycle. Data that once required days of manual assembly is now processed in minutes. The monetization impact was equally significant. Morrow Renewables can now submit to EMTS within one to two days after the month-end.

Reduced Regulatory Exposure

The shift to automated, protocol-aligned validation also changed the risk profile of the monthly submission. Errors that previously passed undetected through the manual process are now caught before submission, when they are still straightforward to address. The compliance team now submits data with greater confidence and reduced risk.

About Rimba AI Rimba AI provides compliance and operations intelligence for the renewable natural gas industry. The platform connects to SCADA and historian systems, automates RFS reporting workflows, and delivers continuous data visibility into plant performance and credit generation. Learn more at rimba.ai.