Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic strength make them difficult to apply in a hydroponic system. To minimize these deficiencies, we combined the multivariate standard addition (MSAM) sampling technique with the deep kernel learning (DKL) model for a six ISEs array to increase the prediction accuracy and precision of eight ions, including $N{O}_3^{-}$, $N{H}_4^{+}$, $K^{+}$, $C{a}^{2+}$, $N{a}^{+}$, $C{l}^{-}$, $H_{2}P{O}_4^{-}$, and $M{g}^{2+}$. The enhanced data feature based on feature enrichment (FE) of the MSAM technique provided more useful information to DKL for improving the prediction reliability of the available ISE ions and enhanced the detection of unavailable ISE ions (phosphate and magnesium). The results showed that the combined MSAM–feature enrichment (FE)–DKL sensing structure for validating ten real hydroponic samples achieved low root mean square errors (RMSE) of 63.8, 8.3, 29.2, 18.5, 11.8, and 8.8 $\mathrm{mg}·{\mathrm{L}}^{-1}$ with below 8% coefficients of variation (CVs) for predicting nitrate, ammonium, potassium, calcium, sodium, and chloride, respectively. Moreover, the prediction of phosphate and magnesium in the ranges of 5–275 mg·L⁻¹ and 10–80 $\mathrm{mg}·{\mathrm{L}}^{-1}$ had RMSEs of 29.6 and 8.7 $\mathrm{mg}·{\mathrm{L}}^{-1}$ respectively. The results prove that the proposed approach can be applied successfully to improve the accuracy and feasibility of ISEs in a closed hydroponic system.

Electrical energy is the highest energetic cost in recirculation aquaponic systems (RASs), especially for fish-tank water. Therefore, reducing energy consumption is one of the challenges in developing RAS models. In this study, eleven experimental setups, based on numerical models from an earlier investigator, were built to investigate. Among them, three additional cases (cases 9–11) investigated the transient discharging energy efficiency of thermal energy storage (TES). Cases 9–11 considered three temperature levels, namely, 65–75 °C, 71–81 °C, and 81–87 °C, with a mass flow rate of 0.166 kg/s. The results show that when heating 3.4 m³ of fish tank water from 24.5 °C to 28 °C, the average temperature error of the tank water was between 1.2% and 3.4%. The difference in the heat transfer rate was within ±4.2%. The error in the thermal efficiency was below 8.0%. The error range of the total required thermal energy was from 6.4% to 11.5%. Cases 9–11 used 5.6%, 6.4%, and 7.2% of the thermal energy of the TES tank, respectively. The electrical energy consumption was low compared to the thermal energy of the fish tank water received from the TES unit. Therefore, the proposed low-energy-consumption heating method can replace electric heaters.