Publications

Observability-based sensor selection in fish ponds: Application to pond aquaculture in Indonesia

Inderaja, Bagoes M.; Tarigan, Nurhayati Br; Verdegem, Marc C.J.; Keesman, Karel J.

Summary

Water quality plays an important role in aquaculture since it affects the growth, survival, and production of aquaculture species. Consequently, measurement devices are needed to monitor water quality. However, this requires significant capital and extensive knowledge of the farmers, especially for smallholder farmers in developing tropical countries like Indonesia. As a cheaper alternative to using only hardware sensors, soft(ware) sensors may be used, as well. However, before designing feasible soft sensors, a so-called (theoretical) observability analysis needs to be done, where observability is a measure of how well internal states of a system can be inferred from measured system inputs and outputs. The aim of this study was to investigate the selection of sensors, such that a full reconstruction of the internal pond constituents, in tropical fish ponds, from the selected external sensor outputs can be realized at any time. A system theoretical observability analysis of a published antecedent dynamic model, describing the complex interactions between the pond constituents (states), was conducted to determine the minimum set of sensors that makes the pond system fully observable, thus in principle allowing a full reconstruction of all states at any time. Using only a DO sensor does not suffice. The minimum set of sensors that guarantees full observability of the pond system were two during the day and three during the night. The observability analysis showed that 11 possible combinations of two sensors provide a fully observable system during the day. In contrast, only one combination of sensors, that is CO2, NO3 and phosphorus, guarantees a fully observable system during day and night. Observability analysis is crucial for understanding the systems’ behaviour and sensor selection, and supports the design of reliable soft sensors for better control and management of fish ponds.