Implementation of a Floating Head Pressure Condensation Control to Reduce Electrical Energy Consumption in an Industrial Refrigeration System

The growing global demand for energy and the costly taxes on electric energy demonstrate the importance of seeking new techniques to improve energy efficiency in industrial facilities. Refrigeration units demand a large amount of electricity due to the high power needs of the components of the system. One strategy to reduce the electric energy consumption in these facilities is pressure condensation control. The objective here was to develop a logical control model where the physical quantities in the thermodynamic process can be monitored and used to determine the optimum point of the condensation pressure and the mass flow rate of the air in the evaporative condenser. The algorithm developed was validated through experiments and was posteriorly implemented in an ammonia industrial system of refrigeration over a period of sixteen months (480 days). The results showed that the operation of the evaporative condenser with a controlled air mass flow rate by logical modeling achieved a reduction of 7.5% in the consumption of electric energy, leading to a significant reduction in the operational cost of the refrigeration plant.

Improve the Energy Efficiency of the Cooling System by Slide Regulating the Capacity of Refrigerator Compressors

A fundamental part of the electric consumption of the main industries of the food sector comes from the refrigeration production, needed in all production phases. Therefore, every measure that aims to optimize the electric consumption and increase the efficiency of centralized industrial refrigeration systems will help the energetic waste of the company, improving reliability and maintenance. Acting on the regulation of capacity of power compressors used can be a good way to save energy. This article shows a case studied by the authors in an industrial company in the meat industry in Spain, where the refrigeration systems have a great importance in the productive process. It displays the methodology used, the description of the taken actions and the results obtained. These combined measures brought about an improvement, with an energetic saving value reaching 400 MWh per year, leading to an equivalent in CO2 emission reduction of 147.9 tons.

Application of Artificial intelligence to high education: empowerment of flipped classroom with just-in-time teaching

In the so-called society 4.0, Artificial Intelligence (AI) is being widely used in many areas of life. Machine learning uses mathematical algorithms based on "training data", which are able to make predictions or take decisions with the ability to change their behavior through a self-training approach. Furthermore, thanks to AI, a large volume of data can be now processed with the overall goal to extract patterns and transform the information into a comprehensible structure for further utilization, which manually done by humans would easily take several years. In this framework, this article explores the potential of AI and machine learning to empower flipped classroom with just-in-time teaching (JiTT). JiTT is a pedagogical method that can be easily combined with the reverse teaching. It allows professors to receive feedback from students before class, so they may be able to adapt the lesson flow, as well as preparing strategies and activities focused on the student deficiencies. This research explores the application of AI in high education as a tool to analyze the key variables involved in the learning process of students and to integrate JiTT within the flipped classroom. Finally, a case of application of this methodology is presented, applied to the course of Industrial Refrigeration taught at the Polytechnic University of Valencia.

The expert assessment of meat mass loss in the refrigeration chambers of meat and meat processing enterprises

The monitoring of beef meat losses in halves and quarters occurring during cooling technological processes at the industrial refrigeration enterprises depending on different technological factors is given in the article. These factors are: quick and accelerating one-stage cooling, quick twostage cooling, continuous and cycled modes of refrigeration chamber during cooling and storing, besides, the age of cattle, quality groups of meat, average temperature of freezing and the way of cooling the chamber, its defrosting and subsequent storage, duration of moisture drainage after wet cleaning and etc. It is shown that there is inconsistency of factual and normative meat losses due to results of practical work of chambers for cooling and storing at some enterprises of the agricultural-industrial complex. Measures and ways on further improvement of normative and standard base with perspective of development and introducing of the individual standards of meat losses in the existing conditions of refrigeration chamber for each enterprise.

Improving the Energy Efficiency of Industrial Refrigeration Systems by Means of Data-Driven Load Management

A common denominator in the vast majority of processes in the food industry is refrigeration. Such systems guarantee the quality and the requisites of the final product at the expense of high amounts of energy. In this regard, the new Industry 4.0 framework provides the required data to develop new data-based methodologies to reduce such energy expenditure concern. Focusing in this issue, this paper proposes a data-driven methodology which improves the efficiency of the refrigeration systems acting on the load side. The solution approaches the problem with a novel load management methodology that considers the estimation of the individual load consumption and the necessary robustness to be applicable in highly variable industrial environments. Thus, the refrigeration system efficiency can be enhanced while maintaining the product in the desired conditions. The experimental results of the methodology demonstrate the ability to reduce the electrical consumption of the compressors by 17% as well as a 77% reduction in the operation time of two compressors working in parallel, a fact that enlarges the machines life. Furthermore, these promising savings are obtained without compromising the temperature requirements of each load.

A Data-Driven-Based Industrial Refrigeration Optimization Method Considering Demand Forecasting

One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to find near-optimal PLR setpoints in each operation condition. Finally, the proposed method employs a forecasting strategy to manage the compressor switching situations. Thus, undesirable starts and stops of the machine are avoided, preserving its remaining useful life and being more efficient. An experimental validation in a real industrial system is performed in order to validate the suitability and the performance of the methodology. The proposed methodology improves refrigeration system efficiency up to 8%, depending on the operating conditions. The results obtained validates the feasibility of applying data-driven techniques for the optimal control of refrigeration system compressors to increase its efficiency.

Easer/Easer. A planning tool for energy-optimized refrigeration and heat supply systems

Industrielle Kälte- und Wärmeversorgungssysteme haben mit 75 % einen erheblichen Anteil am Gesamtenergiebedarf der deutschen Industrie. Der zunehmende Anteil erneuerbarer volatiler Energien führt dazu, dass die Energieflexibilität der Anlagen an Bedeutung gewinnt. Mit „Easer“ wird in diesem Beitrag das Konzept eines webbasierten Planungswerkzeugs vorgestellt, das die Energieeffizienz und Energieflexibilität im Sinne eines ganzheitlichen Demand Side Management berücksichtigt.   Industrial refrigeration and heat supply systems account for a substantial 75 % of the total energy demand of German industry. The increasing share of renewable, volatile energies means that the energy flexibility of the plants is gaining in importance. With Easer, this article presents the concept of a web-based planning tool that takes energy efficiency and energy flexibility into account in the sense of holistic demand side management.