Gray Wolf Optimization-Long Short-Term Memory Based Temperature Estimation and Closed-Loop Control Method in Microfluidic Chemiluminescence Immunoassay

Driven by the rising demand for point-of-care testing (POCT) in aging societies, accurate temperature regulation of reaction solutions has become a core technical bottleneck for miniaturized chemiluminescence immunoassay systems, since conventional indirect control strategies inevitably produce systematic deviations. To tackle this challenge, we present an integrated solution that couples multiphysics simulation, data-driven temperature estimation modeling, and embedded hardware design. We constructed a COMSOL heat transfer model to analyze the thermal performance of the microfluidic chip. Meanwhile, a grey wolf optimization (GWO) enhanced long short-term memory (LSTM) network was developed to infer the unmeasured actual reaction solution temperature based on accessible parameters, including heating voltage, ambient temperature and substrate temperature. The obtained temperature estimation was then fed back to a fuzzy PID controller for closed-loop regulation. Experimental results demonstrated that the GWO-LSTM model limited the estimation error within 0.3 °C, and the steady-state temperature control accuracy reached ±0.2 °C or higher under fluctuating ambient conditions and diverse initial states. For cardiac troponin I (cTnI) detection, the proposed system shortened the incubation duration and reduced the coefficient of variation from 10.77% to 2.69%. This work addresses the key bottleneck restricting precise temperature control in microfluidic chemiluminescence analyzers, which provides robust technical support for the development of next-generation high-performance POCT instruments.