This article aims to address an existing research gap in the study of the most widely used mathematical procedures in the field of automatic control of energy-sensitive industrial processes. In these types of processes, as is the case of cold storage in the food sector or the pharmaceutical industry, applying energy efficiency measures is very risky. This is because the margin of variation in the temperature of the processes is very small, since the product to be manufactured or preserved is very sensitive. This is where the developments of the Internet of the Things showcase their usefulness because they allow measurements to be taken with great accuracy and to verify the effectiveness of energy efficiency proposals. Nevertheless, there are very few studies and developments on automation measures in energetically sensitive industries. This is the research gap that the present work aims to shed light on, proposing a method for optimizing the process of automatic revision in a refrigerated food warehouse. Said method prominently employs control charts, as they allow for a relatively easy set up and require minimal intervention but can be revised manually if so desired. The analysis also includes an auxiliary variable that measures the impact of the variations in the system. Improvements are also provided to the procedures and variables, with which the most commonly used methods of efficiency control in the industry can maintain good results in energy-sensitive industries. Finally, the best selection of charts for the chosen variables is then discussed and justified.

Maritime transport is the most widely used means of transporting goods, and forecasts indicate that it will continue to grow in the coming years, which is why the IMO is regulating energy efficiency and emissions from maritime transport with exhaustive monitoring. The most widely used measure of energy efficiency during operation on a ship is the Energy Efficiency Operational Indicator (EEOI); however, the difficulty in obtaining references for this indicator, together with the great variability that exists between shipowners when choosing terms such as “cargo transported”, among others, means that this operational indicator of energy efficiency in ships does not have the expected results. This work develops a two-phase procedure for the selection of representative EEOI values for ships, taking into account parameters such as ship speed, cargo, mode of operation and the subsequent determination of a suitable control system that allows the continuous and real-time implementation of measures to improve energy and environmental efficiency on the ship. The proposed final procedure is effective in terms of improving energy efficiency and emissions, on top of being simple, easily implementable, working in real time and adapting to navigation circumstances while keeping emissions under control.

Energy optimization in cold food storage processes is a complex issue, as many variables have to be taken into consideration, according to the nature of the food to be stored. In addition, energy optimization requires exhaustive supervision, especially of the cooling temperatures of the different types of food, as refrigeration is essential for maintaining optimum storage, minimizing product losses. The goal of this study is to present the use of digitization of a process to carry out energy control in real time so that, with a reasonable investment, the quality of the product is maintained and the economic profitability of the facilities is improved. To this aim, we use Industry 4.0 techniques, together with programmable mathematical algorithms in cloud platforms, to build a Digital Twin of a refrigerated food warehouse that will allow automatic supervision of food storage conditions and consumption, as well as to optimize cold generation and the profitability of the process throughout the facility. Keywords: Digital Twin, Smart and sustainable Warehouse, IoT La optimización energética en los procesos de almacenamiento en frío de alimentos es un tema complejo, ya que hay que tener en cuenta muchas variables según la naturaleza de los alimentos a almacenar. Además, la optimización energética requiere una supervisión exhaustiva, especialmente de las temperaturas de enfriamiento de los diferentes tipos de alimentos, ya que la refrigeración es esencial para mantener un almacenamiento óptimo y minimizar las pérdidas de producto. El objetivo de este estudio es presentar el uso de la digitalización de un proceso para realizar el control energético de éste en tiempo real. De tal forma que, con una inversión razonable, se mantenga la calidad del producto y se mejore la rentabilidad económica de las instalaciones. Para ello, utilizamos técnicas de industria 4.0, junto con algoritmos matemáticos programables en plataformas cloud, para construir un Gemelo Digital de un almacén de alimentos refrigerado que permitirá la supervisión automática de las condiciones de almacenamiento de alimentos y el consumo, así como optimizar la generación de frío y la rentabilidad del proceso en toda la instalación. Palabras clave: Gemelo digital, Almacén inteligente y sostenible, IoT

Maintenance operations represent one of the most underutilized opportunities to reduce emissions and improve the energy efficiency of ships. This study proposes an innovative approach that analyzes such interventions from a holistic perspective of energy, environment, and economics using real operational data from two liquefied natural gas (LNG) carriers before and after their maintenance operations. The results show that comprehensive actions such as complete hull and propeller cleaning can reduce fuel consumption by more than 30% and CO₂ emissions by more than 15%, in addition to improving propulsive efficiency by between 18% and 34%. In contrast, minor interventions, such as underwater propeller cleaning, have a limited effect with very specific improvements in fuel savings at certain speed ranges, but no significant effect on emissions or shaft power. In particular, the study demonstrates that a single comprehensive maintenance operation can change the Carbon Intensity Indicator (CII) rating from category E to D, reinforcing the strategic role of maintenance in the decarbonization and revaluation of maritime transport.