Potential failure modes in RNG project development

  • Potential failure modes in RNG project development

    Posted by Fatma on 2 March 2026 at 2:08 pm

    What are the top 3 failure modes you see in RNG projects between FEED and commissioning—and which ones are still under‑estimated today?

    Peter replied 3 weeks, 5 days ago 7 Members · 13 Replies
  • 13 Replies
  • Peter

    Member
    2 March 2026 at 4:55 pm

    Not sure it they are the top three, but three I think are worth mentioning.

    1) Lack of RNG off-take certainty slowing FID

    2) Insufficient detail in initial design basis leads to change orders, and delays

    3) Regulatory delays related to environmental compliance, gas safety approvals, and pipeline interconnection

    • Peter

      Member
      2 March 2026 at 4:58 pm

      Once at the commissioning phase there can still be many challenges some covered in our article <b itemprop=”headline”>Digester Startup and Commissioning https://azuraassociates.com/digester-startup-and-commisioning/.

    • Peter

      Member
      2 March 2026 at 5:02 pm

      @atkinsrealis_fatma What sort of RNG projects do you typically encounter?

      • Fatma

        Member
        4 March 2026 at 8:26 am

        We typically encounter a broad range of renewable natural gas (RNG) projects across the full project lifecycle. Most commonly, these include:

        – Anaerobic digestion–based RNG projects treating organic waste streams such as agricultural residues (dairy, swine, poultry manure), municipal organics, food and beverage waste, and industrial by‑products.

        – Landfill gas–to‑RNG projects, including gas collection system optimization, upgrading facilities, and pipeline interconnection.

        – Wastewater treatment plant (WWTP) RNG projects, where biogas from sludge digestion is upgraded for injection or end use.

        – Biogas upgrading and conditioning facilities, covering technologies such as membrane separation, PSA, and amine systems to meet pipeline or vehicle fuel specifications.

        – Pipeline injection and interconnection scopes, including metering, compression, odorization, and regulatory compliance.

        – End‑use applications, such as RNG for utility grid injection, transportation fuel (CNG/LNG), or industrial thermal loads.

        – Brownfield upgrades and debottlenecking, where existing biogas or energy‑from‑waste assets are retrofitted to enable RNG production.

        Our involvement often spans feasibility and pre‑FEED, FEED, detailed engineering, permitting support, owner’s engineering, and EPC /commissioning support, depending on the client’s needs and project maturity.

        • Peter

          Member
          5 March 2026 at 9:50 am

          Thanks Fatma, With your broad view of the industry where do you see project get hung up after they have a business case that looks promising?

  • Tejas

    Member
    3 March 2026 at 6:43 am

    Hi Fatma,

    Top 3 potential failures in RNG project in India are:

    1. Improper planned feedstock supply chain

    2. Selection of wrong technology

    3. Off-take of RNG

    • Natalia Bourenane

      Organizer
      4 March 2026 at 10:28 am

      Hi Tejas, we had just finished the webinar on European biogas sector and one of the panellists mentioned that one of the reasons of project failures in India is the need and desire of lower project investment at the start (as compared to European projects). What do you think about that?

      • Vanita

        Member
        5 March 2026 at 3:56 am

        Biogas plant failure in India is rarely due to wrong technology selection alone but more because of budget constraint leading to compromised plant design. The real issue is inadequate systems engineering. A successful biogas project requires the integration of feedstock logistics, pretreatment, digester design, microbial process control, gas upgrading, and digestate management. Failure of any one subsystem destabilizes the entire biological process, leading to poor performance or plant shutdown.

        • Natalia Bourenane

          Organizer
          5 March 2026 at 7:26 am

          Vanita, thank you for your insights!

  • Tejas

    Member
    4 March 2026 at 10:36 am

    Oh Yes, Natalia,

    To lower project investment wrong technology is selected and reason to failure of project.

  • Nikan

    Member
    4 March 2026 at 5:39 pm

    From what we see across RNG projects, three issues tend to create the biggest problems between FEED and commissioning. First is feedstock variability and supply assumptions. Many projects model stable manure or organic waste volumes during FEED, but in reality seasonal variability, contamination, or competing waste markets can significantly affect digester performance and gas yield ( Mind those tipping fees!). Second is interconnection and gas quality compliance. Pipeline injection requirements such as methane concentration, siloxane removal, or pressure specifications are often more complex than expected, and delays in upgrading equipment or utility approvals can push commissioning timelines. Third is permitting and regulatory alignment, which is still frequently underestimated. Projects can face unexpected delays related to environmental permits, digestate management approvals, or aligning with programs like carbon or clean fuel credit systems.

    Among these, interconnection timelines and regulatory alignment are probably still the most underestimated today because they depend heavily on external stakeholders and processes that developers do not fully control. Reach out to us in CNF and we should be able to help you with so much of these headaches!

    • Dave

      Member
      4 March 2026 at 10:09 pm

      ha ha ha, yes! The reality between academic study, a pretty spreadsheet, an state-of-the-art AI tool trained with sparse and poorly characterized data, and real world experience is huge. We’ve seen several pre-FEED or early-FEL projects get end up as disputes or expert witness situations because they developers wrongly relied on low quality data.

      They seem to lose the idea of tracking significant figures in their calculations. Just because an AI can calculate to many decimals, does not mean a model is accurate. The difference between precise and accurate is lost on them. That is an expensive lesson they learn over and over again!

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