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Journal of Innovation in Science, Engineering and Technology

Document Type

Original Study

Abstract

The generation of acidic and alkaline laboratory waste remains an enduring safety and environmental problem in educational institutions, research laboratories, and small-scale industrial settings. In many such environments, neutralization is still performed manually through intermittent pH measurement and operator judgement. This practice is slow, inconsistent, and potentially hazardous, particularly when corrosive substances are involved. The present study proposes and evaluates a smart Internet of Things (IoT) prototype that automates the detection, neutralization, and monitoring of laboratory chemical waste by integrating edge intelligence with secure cloud telemetry.
The proposed system is centered on an ESP32-S3-N16R8 microcontroller, a DFRobot SEN0161 pH sensor, two peristaltic dosing pumps, an L298N motor driver, an SSD1306 OLED display, and an alerting subsystem comprising a buzzer and indicators. The platform continuously measures the pH of liquid waste, classifies it as acidic, basic, or neutral, and activates a suitable dosing action until the waste reaches a safe neutralization band. To improve measurement stability, the prototype employs an Exponential Moving Average (EMA) filter over the 12-bit analogue-to-digital conversion output. Secure cloud communication is established via Azure IoT Hub using MQTT over TLS, with telemetry also mirrored to Blynk for live visualization. The code architecture includes sensor acquisition, edge-based threshold logic, event-driven updates, Azure message queuing, and user-facing feedback through both local and remote interfaces.
Empirical testing with acidic and basic samples indicates that the prototype achieved a high degree of neutralization accuracy, with representative final pH values near neutrality and cloud delivery fidelity approaching 98.5%. End-user feedback from two non-group participants indicated that the system was easy to understand, reduced manual effort, and improved situational awareness, though improvements were required in OLED readability and neutralization speed. Iterative refinement led to a second prototype version featuring adjustable pump speed variables, a buzzer alert, improved display messages, and configurable neutral pH thresholds. The study concludes that the ESP32-S3-based architecture provides a viable low-cost platform for intelligent hazardous liquid waste management in laboratory environments. It further demonstrates how edge computing, closed-loop sensing, and cloud analytics can be combined to enhance safety, reduce human exposure, and establish a foundation for future adaptive control strategies.

First Pages

78

Last Page

87

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