The scope of this study is the evaluation of early detection methods for cavitation phenomena in centrifugal irrigation pumps by analyzing the produced vibration and sound signals from a low-cost sensor and data acquisition system and comparing several computational methods. Vibration data was acquired using the embedded accelerometer sensor of a smartphone device. Sound signals were obtained using the embedded microphone of the same commercial smartphone. The analysis was based on comparing the signals in different operating conditions with reference to the best efficiency operating point of the pump. In the case of vibrations, data was acquired for all three directional axes. The signals were processed by computational methods to extract the relative features in the frequency domain and use them to train an artificial neural network to be able to identify the different pump operating conditions while the cavitation phenomenon evolves. Three different classification algorithms were used to examine the most preferable approach for classifying data, namely the Classification Tree, the K-Nearest Neighbor, and the Support Vector Data algorithms. In addition, a convolutional neural network was utilized to examine the success rate of the classification when the datasets were formed as spectrograms instead. A detailed comparison of the classification algorithms and different axes was conducted. Comparing the results of the different methods for vibration and sound datasets, classification accuracy showed that in the case of vibration, the detection of cavitation in real conditions is possible, while it proves more challenging to identify cavitation conditions using sound data obtained with low-cost commercial sensors.

Ammonia accumulation in biogas plants reactors is becoming more frequently encountered, resulting in reduced methane (CH₄) production. Ammonia toxicity occurs when N-rich substrates represent a significant part of the biogas plant’s feedstock. The aim of this study was to develop an estimation method for the effect of ammonia toxicity on the CH₄ production of biogas plants. Two periods where a biogas plant operated at 3200 mg·L⁻¹ (1st period) and 4400 mg·L⁻¹ (2nd period) of ammonium nitrogen (NH₄⁺–N) were examined. Biomethane potentials (BMPs) of the individual substrates collected during these periods and of the mixture of substrates with the weight ratio used by the biogas plant under different ammonia levels (2000–5200 mg·L⁻¹ NH₄⁺–N) were determined. CH₄ production calculated from the substrates’ BMPs and the quantities used of each substrate by the biogas plant was compared with actual CH₄ production on-site. Biogas plant’s CH₄ production was 9.9% lower in the 1st and 20.3% in the 2nd period in comparison with the BMP calculated CH₄ production, of which 3% and 14% was due to ammonia toxicity, respectively. BMPs of the mixtures showed that the actual CH₄ reduction rate of the biogas plant could be approximately estimated by the ammonia concentrations levels.

The production cost of greenhouse cultivation depends mainly upon significant amounts of energy consumption in order to keep the optimum environmental condition for plant growth. Τhe expenditure on energy, either for heating or cooling, ranges between 30% to 60% of the total production costs, and any attempt to save energy will result in a positive effect on the potentiality of production accordingly, affecting the greenhouse product prices. Research has shown that CO₂ enrichment in greenhouses significantly increases the yield of most indoor cultivation of plants of the C3 category. For these plants, when the CO₂ concentration increases by three times above that of the atmosphere (380 ppm), the optimum plant growth temperature shifts higher by 5 °C to 10 °C reaching up to 30 °C to 32 °C. Therefore, huge amounts of solar energy can be captured inside the greenhouses, as the ventilation can be decreased. Alongside this, the use of a simple passive solar system consisting of plastic sleeves filled with water is considered to be an improved way to increase the energy inside greenhouses. In this work, three experimental trials were conducted to examine the benefit of the solar energy captured inside a greenhouse during CO₂ enrichment at high temperatures. Finally, a modeling approach based on the heat loss equation was developed in order to establish the energy saving inside the greenhouses under the circumstances mentioned.