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<p>Operational inefficiencies in wastewater management systems increasingly lead to treatment delays, hydraulic overloads, resource wastage, and contamination events. Digital Twin (DT) technology provides a systematic approach to addressing these challenges through continuous physical–virtual synchronization, predictive analytics, and scenario-based decision support. This study presents a DT-assisted, real-time monitoring and forecasting framework for wastewater infrastructure, integrating IoT-enabled sensing, EPANET–MATLAB based hydraulic simulation, hybrid ANFIS-driven predictive intelligence, and a consortium blockchain for secure state attestation and auditability. The proposed framework was evaluated using two real-world time-series datasets obtained from Kazhydromet and affiliated municipal wastewater facilities, comprising a total of 80,114 samples that capture both internal operational telemetry and external environmental drivers. Experimental results demonstrate stable physical–virtual alignment with bounded synchronization errors and no cumulative drift over extended operation. The control-path end-to-end latency was measured at approximately <b>9.51&#xa0;s</b> for operational data and <b>11.01&#xa0;s</b> for environmental data, confirming near-real-time responsiveness suitable for supervisory wastewater control. When security-path operations are considered, the worst-case secure latency remains bounded below <b>18&#xa0;s</b>, with blockchain consensus executed asynchronously to avoid interference with time-critical decision loops. From a diagnostic perspective, the proposed DT–ANFIS model achieved consistently superior performance compared to generic machine learning baselines and process-rule based simulators. Across operational datasets, the framework attained a <b>precision of 89.3%</b>, <b>sensitivity of 88.1%</b>, <b>specificity of 90.2%</b>, and an <b>F-measure of 88.7%</b>, with statistically significant improvements (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p &lt; 0.05\)</EquationSource> </InlineEquation>) and narrow 95% confidence intervals. Predictive evaluation further confirmed realistic forecasting capability, achieving a Pearson correlation coefficient of up to <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(r^2 = 0.89\)</EquationSource> </InlineEquation> with reduced prediction errors relative to recent wastewater modeling studies.</p>

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Smart wastewater management in hydro-technical systems using digital twin technology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月7日

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  • Tariq Ahamed Ahanger,
  • Zhanuzak Abdibayev,
  • Saule Sagnayeva,
  • Ainur Zhumadillayeva,
  • Kanagat Dyussekeyev

摘要

Operational inefficiencies in wastewater management systems increasingly lead to treatment delays, hydraulic overloads, resource wastage, and contamination events. Digital Twin (DT) technology provides a systematic approach to addressing these challenges through continuous physical–virtual synchronization, predictive analytics, and scenario-based decision support. This study presents a DT-assisted, real-time monitoring and forecasting framework for wastewater infrastructure, integrating IoT-enabled sensing, EPANET–MATLAB based hydraulic simulation, hybrid ANFIS-driven predictive intelligence, and a consortium blockchain for secure state attestation and auditability. The proposed framework was evaluated using two real-world time-series datasets obtained from Kazhydromet and affiliated municipal wastewater facilities, comprising a total of 80,114 samples that capture both internal operational telemetry and external environmental drivers. Experimental results demonstrate stable physical–virtual alignment with bounded synchronization errors and no cumulative drift over extended operation. The control-path end-to-end latency was measured at approximately 9.51 s for operational data and 11.01 s for environmental data, confirming near-real-time responsiveness suitable for supervisory wastewater control. When security-path operations are considered, the worst-case secure latency remains bounded below 18 s, with blockchain consensus executed asynchronously to avoid interference with time-critical decision loops. From a diagnostic perspective, the proposed DT–ANFIS model achieved consistently superior performance compared to generic machine learning baselines and process-rule based simulators. Across operational datasets, the framework attained a precision of 89.3%, sensitivity of 88.1%, specificity of 90.2%, and an F-measure of 88.7%, with statistically significant improvements ( \(p < 0.05\) ) and narrow 95% confidence intervals. Predictive evaluation further confirmed realistic forecasting capability, achieving a Pearson correlation coefficient of up to \(r^2 = 0.89\) with reduced prediction errors relative to recent wastewater modeling studies.