IoT-Enabled Monitoring of AC Condensate Water for Quality Assessment and Early Detection of HVAC System Health

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 16 | 01 | Page :
    By

    Vivek Yadav,

  • Aman Bharati,

  • Ms. Anamika Dhawan,

  1. Research Scholar, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India
  2. Research Scholar, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India
  3. Assistant Professor, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India

Abstract

The shortage of water and expensive reactive maintenance of HVAC are major problems in the modern building management. The paper introduces an Internet of Things (IoT)-enabled air conditioning (AC) condensate to water resource (predictive maintenance) and sustainable water reuse. The nature of our approach defines the quality of the condensate water at the baseline and indicates that it contains low levels of total dissolved solids (TDS) and has almost neutral pH characteristics. We present a multi-layered IoT system that is based on low-power technology, MQTT protocol, and cloud-based analytics. One important contribution is shown in the way that water quality parameters can show the health of the system: low pH is an indication of constant acidic corrosion of coils by VOCs, and high turbidity is an indication of the impending clogging of the drain line by biofouling. To detect anomalies, we use a Long Short-Term Memory (LSTM) Autoencoder that learns normal operating patterns and detects anomalies in real time to be able to perform correct anomaly classification and approximate Remaining Useful Life (RUL). The experimental findings indicate that the system has an F1-score of 0.94 when it comes to fault detection and can give 1-3 days of warning before a maintenance event. By doing so, the HVAC maintenance is turned into proactive and informed, as well as cost-efficient, whereas it conserves water resources and makes the operations more affordable.

Keywords: IoT, predictive maintenance, HVAC, condensate water, water quality, LSTM autoencoder, sensor drift, corrosion detection

How to cite this article:
Vivek Yadav, Aman Bharati, Ms. Anamika Dhawan. IoT-Enabled Monitoring of AC Condensate Water for Quality Assessment and Early Detection of HVAC System Health. Journal of Instrumentation Technology & Innovations. 2026; 16(01):-.
How to cite this URL:
Vivek Yadav, Aman Bharati, Ms. Anamika Dhawan. IoT-Enabled Monitoring of AC Condensate Water for Quality Assessment and Early Detection of HVAC System Health. Journal of Instrumentation Technology & Innovations. 2026; 16(01):-. Available from: https://journals.stmjournals.com/joiti/article=2026/view=239603


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Ahead of Print Subscription Review Article
Volume 16
01
Received 30/03/2026
Accepted 31/03/2026
Published 02/04/2026
Publication Time 3 Days
40

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