A quick condition adaptive soft sensor model with dual scale structure for dissolved oxygen simulation of recirculation aquaculture system

Abstract

Dissolved oxygen (DO) is critical to recirculating aquaculture system (RAS), which is holistically affected by many nutritional, physiological, chemical, and fish behavioral processes. Modeling a DO soft sensor is an important means of providing broader sensing coverage for optimized DO control and DO sensor equipment validation. The main problem of DO soft sensor modeling is that stable long-term simulation parameters are usually insensitive to condition changes, resulting in slow adaptation. The second problem is that DO change has multiple influences. To improve condition adaptation performance and decouple multiple influence factors, a dual scale DO soft sensor modeling method is proposed to achieve dynamic condition adaptation during long-term simulation. The upper scale is a direct computer-mapping-based programmable structure (DCM-PS) model that represents the kinetics and equilibrium determined dynamic balance model for long-term simulation. The lower scale consists of recurrent neural networks (RNN) regression sub-models of aeration, feeding, and water flow. The RNN regression sub-models integrate the full connection network with RNN regression, which outputs frequency domain distributions of different conditions and predicts short-term DO expectations caused by condition change. To realize quick condition adaptation of the dual scale soft sensor model, a parameter update procedure is proposed to adapt upper scale simulation parameters to new conditions by comparing DCM-PS simulated result with RNN sub-model expectations. The instance with a sudden condition change proved that the dual scale model was able to implement quick adaptation to the changed operational condition of the RAS. The proposed method shows promise as a quick self-validation for DO sensor diagnosis, as well as for real time optimized control of DO and other parameters.