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<p>In recent years, we have seen a growing need for energy, which has had environmental consequences through the use of fossil fuels. Some of the sectors of our society make intensive use of energy, as is the case with wastewater treatment plants (WWTPs). Through anaerobic digestion, these infrastructures can produce energy, therefore improve energy efficiency and decrease the environmental footprint. This study aims to design, tune and evaluate a hybrid deep learning (DL) model, called incremental dual path network (IDPN), to forecast energy production in an anaerobic reactor for the next two days. The hybrid model’s performance was compared against five DL models conceived: long short-term memory (LSTMs), multi-layer perception (MLP), gated recurrent units (GRUs), Transformers and convolutional neural networks (CNNs). Furthermore, two data processing strategies were applied due to system failures and missing values. Four model evaluation metrics and the obtained results show that the hybrid model, which combines LSTMs and CNNs, presented the best performance in both approaches of data processing, with the best candidate model presenting a mean absolute error (MAE) of 312.1 kWh, root mean squared error (RMSE) of 341.6 kWh, mean absolute percentage error (MAPE) of 15.9% and R<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="521_2025_11545_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>2</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> of 0.95. Following this, an ablation study was conducted, demonstrating that across several variations, the baseline IDPN consistently achieved the best results. Moreover, in both approaches, the removal or modification of the CNN led to a severe decline in performance, surpassing the impact of altering the LSTM, reinforcing its importance in the model’s architecture.</p>

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Incdualpathnet : a hybrid architecture proposal for predicting energy production in a wastewater treatment plants

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月15日

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  • Pedro Oliveira,
  • Francisco S. Marcondes,
  • Maria Salomé Duarte,
  • Dalila Durães,
  • Cristina Gonçalves,
  • Gilberto Martins,
  • Paulo Novais

摘要

In recent years, we have seen a growing need for energy, which has had environmental consequences through the use of fossil fuels. Some of the sectors of our society make intensive use of energy, as is the case with wastewater treatment plants (WWTPs). Through anaerobic digestion, these infrastructures can produce energy, therefore improve energy efficiency and decrease the environmental footprint. This study aims to design, tune and evaluate a hybrid deep learning (DL) model, called incremental dual path network (IDPN), to forecast energy production in an anaerobic reactor for the next two days. The hybrid model’s performance was compared against five DL models conceived: long short-term memory (LSTMs), multi-layer perception (MLP), gated recurrent units (GRUs), Transformers and convolutional neural networks (CNNs). Furthermore, two data processing strategies were applied due to system failures and missing values. Four model evaluation metrics and the obtained results show that the hybrid model, which combines LSTMs and CNNs, presented the best performance in both approaches of data processing, with the best candidate model presenting a mean absolute error (MAE) of 312.1 kWh, root mean squared error (RMSE) of 341.6 kWh, mean absolute percentage error (MAPE) of 15.9% and R \(^{2}\) 2 of 0.95. Following this, an ablation study was conducted, demonstrating that across several variations, the baseline IDPN consistently achieved the best results. Moreover, in both approaches, the removal or modification of the CNN led to a severe decline in performance, surpassing the impact of altering the LSTM, reinforcing its importance in the model’s architecture.