Effective modeling and management are critical in wastewater treatment facilities since the aeration process accounts for 65–70% of the overall energy consumption. This study assesses control strategies specifically designed for different sizes of WWTP, analyzing their economic, environmental, and energy-related effects. Small WWTPs see advantages from the utilization of on/off and proportional–integral–derivative (PID) control methods, resulting in 10–25% energy savings and the reduction in dissolved oxygen (DO) levels by 5–30%. Cascade control and model predictive control (MPC) improve energy efficiency by 15–30% and stabilize DO levels by 15–35% in medium-sized WWTPs. Advanced WWTPs that utilize technologies such as MPC integrated with artificial intelligence (AI) and machine learning (ML) can decrease energy usage by 30–40% and enhance DO levels by 35–40%. Life cycle assessment (LCA) demonstrates substantial decreases in greenhouse gas (GHG) emissions: 5–20% for small, 10–25% for medium, and 30–35% for large WWTPs. These findings illustrate the feasibility and expandability of these tactics in both controlled laboratory environments and real-world situations, emphasizing the significance of customized methods for improving energy efficiency and sustainability in wastewater treatment. Subsequent investigations should prioritize integrating renewable energy sources and resolving obstacles in developing nations to enhance wastewater treatment plants’ energy efficiency and sustainability.

Investigations into the structures of gas hydrates, the mechanisms of formation, and dissociation with modern instruments on the experimental aspects, including Raman, X-ray, XRD, X-CT, MRI, and pore networks, and numerical analyses, including CFD, LBM, and MD, were carried out. The gas hydrate characteristics for dissociation and formation are multi-phase and multi-component complexes. Therefore, it was important to carry out a comprehensive investigation to improve the concept of mechanisms involved in microscale porous media, emphasizing micro-modeling experiments, 3D imaging, and pore network modeling. This article reviewed the studies, carried out to date, regarding conditions surrounding hydrate dissociation, hydrate formation, and hydrate recovery, especially at the pore-scale phase in numerical simulations. The purpose of visualizing pores in microscale sediments is to obtain a robust analysis to apply the gas hydrate exploitation technique. The observed parameters, including temperature, pressure, concentration, porosity, saturation rate, and permeability, etc., present an interrelationship, to achieve an accurate production process method and recovery of gas hydrates.

In response to the global pursuit of net-zero carbon emissions, the electrification of agricultural machinery is becoming a significant research and development trend. This study introduces the overall design of a 4 kW air compressor aimed at achieving a green vision for agricultural machinery. The design focuses on providing continuous and stable power and air output using a lithium-ion battery. Durability and cost-effectiveness are prioritized, with a particular emphasis on the Arduino system for integrating battery and motor systems to withstand harsh conditions and ensure ease of maintenance. A permanent magnet brushless motor was selected as the power source, paired with an optimized pulley to supply the proper torque to the air compressor. The system employs an Arduino-based feedback control sensor for air pressure regulation, ensuring energy efficiency. The primary energy source is a 48 V lithium iron phosphate battery, known for its high energy density and safety. The battery design focuses on system integration, addressing specific environmental discharge requirements. The embedded battery management system provides thermal and lifecycle parameter estimation, guaranteeing long-duration power supply and safe operation under various conditions. Unlike traditional fuel-driven systems, lithium iron phosphate batteries do not emit harmful gases, aligning with environmental standards. System integration testing demonstrated that the air pressure feedback control effectively meets the energy-saving requirements by digitally reducing power output as air accumulates in the chamber. Bench testing confirmed that the system performs as designed, achieving the desired results and advancing the goal of sustainable agricultural machinery.

This work investigates smartphone-based image processing for monitoring reservoir water quality, driven by the necessity for accessible and economical environmental evaluation techniques. The objective is to examine the correlation between water color and essential water quality parameters, including turbidity, total phosphorus, and chlorophyll-a, utilizing basic, readily accessible technology. The concept entails taking water photos from three principal reservoirs in Taiwan—Shimen, Liyutan, and Hushan—utilizing a smartphone camera, succeeded by sophisticated image processing algorithms, encompassing RGB color space analysis and ripple filtering. The findings indicate strong correlations between the G/R ratio obtained from the photos and conventional water quality parameters, particularly turbidity and chlorophyll-a. The correlation analysis yielded R² = 0.72 (p < 0.01) for turbidity and R² = 0.68 (p < 0.05) for chlorophyll-a, confirming the statistical significance of the results.

The transition toward renewable-powered greenhouse agriculture offers opportunities for reducing operational costs and environmental impacts, yet challenges remain in managing fluctuating energy loads and optimizing agricultural inputs. While second-life lithium-ion batteries provide a cost-effective energy storage option, their thermal and electrical characteristics under real-world greenhouse conditions are poorly documented. Similarly, although plasma-activated water (PAW) shows potential to reduce chemical fertilizer usage, its integration with renewable-powered systems requires further investigation. This study develops an adaptive monitoring and modeling framework to estimate the thermal resistances (R_u, R_c) and internal resistance (R_int) of second-life lithium-ion batteries using operational data from greenhouse applications, alongside a field trial assessing PAW effects on beefsteak tomato cultivation. The adaptive control algorithm accurately estimated surface temperature (T_s) and core temperature (T_c), achieving a root mean square error (RMSE) of 0.31 °C, a mean absolute error (MAE) of 0.25 °C, and a percentage error of 0.31%. Thermal resistance values stabilized at R_u ≈ 3.00 °C/W (surface to ambient) and R_c ≈ 2.00 °C/W (core to surface), indicating stable thermal regulation under load variations. Internal resistance (R_int) maintained a baseline of ~1.0–1.2 Ω, with peaks up to 12 Ω during load transitions, confirming the importance of continuous monitoring for performance and degradation prevention in second-life applications. The PAW treatment reduced chemical nitrogen fertilizer use by 31.2% without decreasing total nitrogen availability (69.5 mg/L). The NO₃⁻-N concentration in PAW reached 134 mg/L, with an initial pH of 3.04 neutralized before application, ensuring no adverse effects on germination or growth. Leaf nutrient analysis showed lower nitrogen (1.83% vs. 2.28%) and potassium (1.66% vs. 2.17%) compared to the control, but higher magnesium content (0.59% vs. 0.37%), meeting Japanese adequacy standards. The total yield was 7.8 kg/m², with fruit quality comparable between the PAW and control groups. The integration of adaptive battery monitoring with PAW irrigation demonstrates a practical pathway toward energy efficient and sustainable greenhouse operations.