コンポスト化プロセスにおける曝気強度制御へのAI導入によるエネルギー消費削減
Reducing energy consumption through the implementation of AI to control aeration intensity in the composting process. (原題)
R. Sidełko, R. Suszyński, R. Cichowicz, Mikołaj Lisicki, B. Janowska, J. Piekarski
🤖 gxceed AI 要約
日本語
下水汚泥コンポスト施設（ポーランド・Tczew）の実機41サイクルを対象に、LSTMニューラルネットワークで曝気間隔を予測する制御アルゴリズムを開発。有機物分解速度を損なわず、曝気の比電力消費を4.1→3.1 kWh/Mgへ約24%削減した。AIによるプロセス最適化が省エネに有効であることを実証した。
English
An LSTM neural network was developed to predict aeration intervals at a full-scale sewage-sludge composting facility in Tczew, Poland, using 41 industrial cycles of sensor and physicochemical data. The ANN-based control cut specific aeration electricity use from 4.1 to 3.1 kWh/Mg (~24%) without slowing organic matter decomposition, demonstrating AI-driven process optimization for energy savings.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では下水汚泥のコンポスト・資源化と省エネ運営が自治体・水処理事業者の課題。AI制御による電力削減はGX推進・カーボンニュートラル計画に資するが、開示制度との直接連動は弱い。
In the global GX context
Globally, this adds to the evidence base that AI/ML process control can deliver measurable energy and emissions reductions in waste and water utilities, supporting corporate energy-transition and Scope 1/2 reduction targets, though it sits outside disclosure-framework scholarship.
👥 読者別の含意
🔬研究者:AI/MLによる産業プロセス最適化と省エネ効果の実証例として、廃棄物・水処理分野の脱炭素研究に有用。
🏢実務担当者:下水汚泥・廃棄物処理施設の運営チームが曝気制御にAIを導入し電力コストと排出を削減する際の参考事例。
🏛政策担当者:自治体の廃棄物・水処理インフラ省エネ政策やGX投資判断の根拠として活用可能。
📄 Abstract（原文）
The paper presents the results of a study on the use of artificial neural network (ANN) to optimize aeration intensity during the composting of municipal sewage sludge. Field experiments were carried out at a composting facility in Tczew, where a two-stage, six-week composting process was applied to a mixture of sewage sludge and wood chips in concrete reactors with forced aeration. Process monitoring encompassed 41 full-scale composting cycles under industrial conditions. For modeling purposes, a dataset was compiled that included physicochemical input variables (e.g., organic matter content, carbon, nitrogen, and pH) and online sensor data (temperature, moisture content, oxygen concentration, and airflow rate), together with output variables describing fan operation. A deep learning long short-term memory (LSTM) neural network for sequential data analysis was used to predict aeration intervals. The model was trained on 554 cases, approximately 12% of which were reserved for independent testing. The results demonstrated good agreement between ANN predictions and actual operating data, as well as the model's ability to capture the gradual decrease in oxygen demand as the composting process progressed. Under operational conditions, the ANN-based control algorithm reduced the specific electricity consumption for aeration from 4.1 to 3.1 kWh/Mg, corresponding to an approx. 24% reduction compared with conventional interval-based control. The developed model allows for optimizing the operating interval of the compost aeration fan without significantly affecting the rate of organic matter decomposition.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1016/j.jenvman.2026.131042first seen 2026-10-03 05:24:14 · last seen 2026-10-07 05:32:29
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