Data-Monitoring Solution for Desalination Processes: Cooling Tower and Mechanical Vapor Compression Hybrid System.

The advancement of novel water treatment technologies requires the implementation of both accurate data measurement and recording processes. These procedures are essential for acquiring results and conducting thorough analyses to enhance operational efficiency. In addition, accurate sensor data facilitate precise control over chemical treatment dosages, ensuring optimal water quality and corrosion inhibition while minimizing chemical usage and associated costs. Under this framework, this paper describes the sensoring and monitoring solution for a hybrid system based on a cooling tower (CT) connected to mechanical vapor compression (MVC) equipment for desalination and brine concentration purposes. Sensors connected to the data commercial logger solution, Almemo 2890-9, are also discussed in detail such as temperature, relative humidity, pressure, flow rate, etc. The monitoring system allows remote control of the MVC based on a server, GateManager, and TightVNC. In this way, the proposed solution provides remote access to the hybrid system, being able to visualize gathered data in real time. A case study located in Cartagena (Spain) is used to assess the proposed solution. Collected data from temperature transmitters, pneumatic valves, level sensors, and power demand are included and discussed in the paper. These variables allow a subsequent forecasting process to estimate brine concentration values. Different sample times are included in this paper to minimize the collected data from the hybrid system within suitable operation conditions. This solution is suitable to be applied to other desalination processes and locations.

Integrated Heat Recovery System Based on Mixed Ionic-Electronic Conducting Membrane for Improved Solid Oxide Co-Electrolyzer Performance.

The current state of mixed ionic-electronic conducting ceramic membrane technology presents significant advancements with potential applications in various fields including solid oxide electrolyzers, fuel cells, hydrogen production, CO2 reduction, and membrane reactors for chemical production and oxygen separation. Particularly in oxygen separation applications, optimal conditions closely align with the conditions of oxygen-rich air streams emitted from the anode of solid oxide co-electrolyzers. This paper describes and analyzes a novel integrated heat recovery system based on mixed ionic-electronic conducting membranes. The system operates in two stages: firstly, oxygen is separated from the anode output stream using mixed ionic-electronic conducting membranes aided by a vacuum system, followed by the heat recovery process. Upon oxygen separation, the swept gas stream is recirculated at temperatures near thermoneutral conditions, resulting in performance improvements at both cell and system levels. Additionally, an oxygen stream is generated for various applications. An Aspen HYSYS® model has been developed to calculate heat and material balances, demonstrating the efficiency enhancements of the proposed system configuration.

A Low-Cost Hardware Architecture for EV Battery Cell Characterization Using an IoT-Based Platform.

Since 1997, when the first hybrid vehicle was launched on the market, until today, the number of NIMH batteries that have been discarded due to their obsolescence has not stopped increasing, with an even faster rate more recently due to the progressive disappearance of thermal vehicles on the market. The battery technologies used are mostly NIMH for hybrid vehicles and Li ion for pure electric vehicles, making recycling difficult due to the hazardous materials they contain. For this reason, and with the aim of extending the life of the batteries, even including a second life within electric vehicle applications, this paper describes and evaluates a low-cost system to characterize individual cells of commercial electric vehicle batteries by identifying such abnormally performing cells that are out of use, minimizing regeneration costs in a more sustainable manner. A platform based on the IoT technology is developed, allowing the automation of charging and discharging cycles of each independent cell according to some parameters given by the user, and monitoring the real-time data of such battery cells. A case study based on a commercial Toyota Prius battery is also included in the paper. The results show the suitability of the proposed solution as an alternative way to characterize individual cells for subsequent electric vehicle applications, decreasing operating costs and providing an autonomous, flexible, and reliable system.

Optimizing the environmental sustainability of alternative post-harvest scenarios for fresh vegetables: A case study in Spain.

The aim of this research is to define different scenarios that optimize the environmental sustainability of the post-harvest stage of vegetable products (cauliflower and brassicas mix). These scenarios considered different packaging materials; energy generation technologies for the processing plant (standard electricity mix vs. renewable options); organic waste management (composting, anaerobic digestion, and animal feeding); and refrigerated transportation (local, national, and international, using diesel, natural gas, and hybrid trucks and railway). The analysis has been carried out based on a foreground inventory provided by a company that operating internationally, in accordance with the International Organization for Standardization (ISO) 14,040 methodological framework and following the latest Product Environmental Footprint (PEF) protocols. The analysis describes four midpoint categories, single score (SS) using EF3.0 life cycle impact assessment (LCIA) methodology and the Cumulative Energy Demand. The carbon footprint (CF) of the post-harvest stage for a base case scenario ranged between 0.24 and 0.29 kg CO2 eq/kg of vegetable, with a strong contribution associated to the production of packaging materials (57.8-65.2 %) and the transport stage (national range in conventional diesel vehicles) (31.5-38.0 %). Comparatively, lower emissions were associated with the energy consumed at the processing factory (up to 4.1 %) while the composting of organic waste management produced some impact savings (up to -3.5 %). Although certain differences were observed, the dominance of the transport stage and the packaging materials is sustained in all the other environmental impact and energy categories evaluated. The most effective measures to reduce the environmental footprint of the post-harvest stage involve: i) using reusable packaging materials; ii) reducing the transport range and using vehicles running on natural gas or hybrid technologies; iii) the incorporation of renewable energy to supply the factory; and iv) the utilization of the organic residues in higher value applications such as animal feeding. Implementing the measures proposed in this study would reduce the post-harvest CF of fresh vegetables by 90 %.

Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT.

Power system configuration and performance are changing very quickly. Under the new paradigm of prosumers and energy communities, grids are increasingly influenced by microgeneration systems connected in both low and medium voltage. In addition, these facilities provide little or no information to distribution and/or transmission system operators, increasing power system management problems. Actually, information is a great asset to manage this new situation. The arrival of affordable and open Internet of Things (IoT) technologies is a remarkable opportunity to overcome these inconveniences allowing for the exchange of information about these plants. In this paper, we propose a monitoring solution applicable to photovoltaic self-consumption or any other microgeneration installation, covering the installations of the so-called 'prosumers' and aiming to provide a tool for local self-consumption monitoring. A detailed description of the proposed system at the hardware level is provided, and extended information on the communication characteristics and data packets is also included. Results of different field test campaigns carried out in real PV self-consumption installations connected to the grid are described and analyzed. It can be affirmed that the proposed solution provides outstanding results in reliability and accuracy, being a popular solution for those who cannot afford professional monitoring platforms.

Sensitive Parameter Analysis for Solar Irradiance Short-Term Forecasting: Application to LoRa-Based Monitoring Technology.

Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case example located in the southeast of Spain is included in the paper to evaluate the proposed analysis. This methodology is applicable to other locations, as well as different LoRa configurations, parameters, and networks structures; providing detailed analysis regarding PV monitoring performances and short-term PV generation forecasting discrepancies.

A Decentralized Wireless Solution to Monitor and Diagnose PV Solar Module Performance Based on Symmetrized-Shifted Gompertz Functions.

This paper proposes and assesses an integrated solution to monitor and diagnose photovoltaic (PV) solar modules based on a decentralized wireless sensor acquisition system. Both DC electrical variables and environmental data are collected at PV module level using low-cost and high-energy efficiency node sensors. Data is real-time processed locally and compared with expected PV module performances obtained by a PV module model based on symmetrized-shifted Gompertz functions (as previously developed and assessed by the authors). Sensor nodes send data to a centralized sink-computing module using a multi-hop wireless sensor network architecture. Such integration thus provides extensive analysis of PV installations, and avoids off-line tests or post-processing processes. In comparison with previous approaches, this solution is enhanced with a low-cost system and non-critical performance constraints, and it is suitable for extensive deployment in PV power plants. Moreover, it is easily implemented in existing PV installations, since no additional wiring is required. The system has been implemented and assessed in a Spanish PV power plant connected to the grid. Results and estimations of PV module performances are also included in the paper.