Towards operational net-zero pathways in industrial wastewater treatment systems using digital decision support tools.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers gain a validated methodology for integrated GHG modeling (Scopes 1-4) and scenario analysis in wastewater systems.

🏢実務担当者:Corporate sustainability teams can adopt the digital tool to balance cost, efficiency, and net-zero targets in industrial water treatment.

🏛政策担当者:Policymakers see how including avoided emissions and biogas injection can reshape net-zero accounting for water infrastructure.

Effective green transition strategies require reliable quantification of greenhouse gas (GHG) emissions and the ability to evaluate alternative operational regimes. In this study, a digital decision support tool (DST) is constructed to assess and meet net-zero targets in water treatment systems (WTS). A set of mathematical models was developed and integrated to replicate the behavior of the largest industrial WTS in Northern Europe, focusing on effluent quality and operational costs. In parallel, a GHG emissions module was constructed to estimate direct emissions (DE, Scope 1), indirect energy-related emissions (ERE, Scope 2), selected / operation-linked upstream and downstream emissions (UDE, derived from a screened subset of Scope 3), and avoided emissions (AE, often referred to as Scope 4). Additional biogenic CO2 emissions (BioE) were quantified within both DE and UDE categories. Four mitigation scenarios were analyzed: (i) carbon refluxing (S1), (ii) carbon recovery and alternative inactivation strategies (S2 and S3), (iii) nitrous oxide (N2O) mitigation (S4 and S5), and (iv) biogas upgrading and direct grid injection (S6 and S7). Simulations demonstrate that the proposed approach accurately reproduces COD, N, P, and S mass balances (9.3% deviation), long term and high frequency dynamic performance profiles (11.3% deviation, NRMSE < 0.16), plant-wide energy consumption and production (5.0% and 3.8% deviations respectively), and operational expenditures (OPEX) (4.3% and 3.9% deviations for revenues and costs). GHG accounting results indicate that DE accounts for 39%, ERE for 9%, and UDE for 52% of total operational emissions corresponding to approximately 5.6 kg CO2-eq/m3. N2O is the dominant contributor to DE (88%), while UDE is primarily driven by downstream sludge treatment (74%). AE from fertilizer and downstream natural-gas substitution fully counterbalanced total GHG releases, resulting in an operational net-zero performance of -0.2 kg CO2-eq/m3 within the defined system boundaries. When BioE is accounted, DE is increased by a factor of 3. S1, S6 and S7 achieve operational net-negative performance up to -2.1 kg CO2-eq/m3 by the raised AE from increased substitution of grid energy and mineral fertilizers. S2, S3, S4 and S5 fail to achieve the same net-zero targets due to a significant rise in N2O emissions driven by altered COD/N ratios and/or excessive energy (heat) and chemical (NaOH) demands. By integrating comprehensive modelling and GHG accounting, the DST supports evidence-based decision-making for industrial stakeholders seeking to optimize resource use, minimize operation-linked emissions, and guide long-term investments in sustainable wastewater infrastructure. Based on the presented results this tool is now used by the company to handle future optimizations.

• semanticscholar https://doi.org/10.1016/j.watres.2026.125639first seen 2026-05-15 17:42:10