• Energy Research

Abstract Industrial systems management is nowadays increasingly devoted to improve the control of most critical production aspects. In this context, the performance of all the systems involved in production processes have to be measured and analysed in order to get better insights in terms of potential production and quality improvements as well as energy savings. In order to evaluate the performance of a certain system, a possible and effective way is to compare it with data from similar systems, thus to conduct a benchmark analysis. In the area of energy management, although the compressed air system (CAS) is one of the most important and energy consuming services within industrial plants, enterprises often have difficulties understanding and appreciating the entity of potential benefits coming from the improvement of its energy efficiency. The present paper aims at developing a new benchmark analysis for compressed air systems in industrial plants. The proposed methodology starts from the KPIs (Key Performance Indicators) already available in the scientific literature for CASs’ energy performance and is mainly based on the analysis of a huge real dataset collected from over 15,000 energy audits made on a wide range of different companies, all related to produced quantity of compressed air and energy consumed by CASs. Collected data present some limitations and related improvements and corrective actions have been undertaken and are presented in the followings. Data analyses have been followed by complementary surveys regarding compressed air systems’ use, maintenance and monitoring practices performed within several Italian enterprises and aimed at enhancing and creating a more reliable baseline for benchmarking.

Abstract Energy consumption control in energy intensive companies is always more considered as a critical activity to continuously improve energy performance. It undoubtedly requires a huge effort in data gathering and analysis, and the amount of these data together with the scarceness of human resources devoted to Energy Management activities who could maintain and update the analyses’ output are often the main barriers to its diffusion in companies. Advanced tools such as software based on machine learning techniques are therefore the key to overcome these barriers and allow an easy but accurate control. This type of systems is able to solve complex problems obtaining reliable results over time, but not to understand when the reliability of the results is declining (a common situation considering energy using systems, often undergoing structural changes) and to automatically adapt itself using a limited amount of training data, so that a completely automatic application is not yet available and the automatic energy consumption control using intelligent systems is still a challenge. This paper presents a whole new approach to energy consumption control, proposing a methodology based on Artificial Neural Networks (ANNs) and aimed at creating an automatic energy consumption control system. First of all, three different structures of neural networks are proposed and trained using a huge amount of data. Three different performance indicators are then used to identify the most suitable structure, which is implemented to create an energy consumption control tool. In addition, considering that huge amount of data are not always available in practice, a method to identify the minimum period of data collection to obtain reliable results and the maximum period of usability is described. The general purpose of the work is to allow the automatic utilization of this kind of tools, so a method to identify a lack of accuracy in the model and two different retraining methods are proposed and compared (Mobile Training and Growing Training). The whole approach is eventually applied to the case study of a tertiary building in Rome (Italy).

Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability.

The recovery of waste heat is a fundamental means of achieving the ambitious medium- and long-term targets set by European and international directives. Despite the large availability of waste heat, especially at low temperatures (<250 °C), the implementation rate of heat recovery interventions is still low, mainly due to non-technical barriers. To overcome this limitation, this work aims to develop two distinct databases containing waste heat recovery case studies and technologies as a novel tool to enhance knowledge transfer in the industrial sector. Through an in-depth analysis of the scientific literature, the two databases’ structures were developed, defining fields and information to collect, and then a preliminary population was performed. Both databases were validated by interacting with companies which operate in the heat recovery technology market and which are possible users of the tools. Those proposed are the first example in the literature of databases completely focused on low-temperature waste heat recovery in the industrial sector and able to provide detailed information on heat exchange and the technologies used. The tools proposed are two key elements in supporting companies in all the phases of a heat recovery intervention: from identifying waste heat to choosing the best technology to be adopted.

The drastic increase in energy prices is becoming a major concern in the plastics industry. Indeed, rubber and plastic manufacturing companies are recognizing the importance of focusing more on energy management to reduce the energy they use and, consequently, stay competitive in regional and global markets. Moreover, in the European panorama, the promotion of energy efficiency, use of renewable sources, and decarbonization are the key elements of the strategy that guides the common effort. Since the publication of the European Directive 2012/27/EU on Energy Efficiency, which established the obligation for large companies to undergo mandatory energy audits every four years, thousands of companies in Italy have performed an energy audit. Using a maturity model developed in collaboration with the Italian Energy, New Technology and Environment Agency, various Italian companies have been analyzed in reference to their energy management to assess the variation provoked by this obligation. From the results of the analysis conducted on a significant sample of companies from the rubber and plastics manufacturing sector, it emerged that on average the companies that have complied with the obligation of energy audit have increased their maturity in their energy management. The analysis was deepened by assessing the degree of coverage of the five maturity levels identified in the model: “Elementary”, “Occasional”, “Project-based”, “Management” and “Optimized”. Moreover, to identify the progress that occurred in the different aspects of Energy Management, the level of development of six different maturity dimensions has been studied: “Strategic approach”, “Awareness, knowledge and skills”, “Methodological approach”, “Organizational structure”, “Energy performance management and Information System”, and “Best practices”. Finally, the observed variations have also been statistically verified through the use of the paired t-test to make statistical inferences about the maturity of the overall population of the plastics industry.

Abstract Measuring and targeting energy performances of manufacturing plants and their subsystems is the first, critical step to understand their energy behavior, to identify energy management opportunities and to evaluate energy savings. In addition, this activity has become always more important in the last decades, as Energy Management Systems and energy management practices based on continuous improvement and people engagement have been largely adopted, requiring the introduction of new energy consumption control systems capable to identify the standard operative energy behavior and performance of systems and machines and their energy baseline, to point out contingent deviations of energy performances from the baseline and to identify possible causes and clearly attribute responsibilities of such deviations. In this context, several attempts have been presented in literature trying to identify methods to create Energy Performance Indicators so as to satisfy these requirements, but most of them are focused either on a physical or on economical definition of the problem (and therefore either on a process/appliance level or on an aggregated level), neglecting the necessity to develop an integrated system to combine those two approaches to the problem and deliver appropriate information to each hierarchical level and function of the company. In addition, even when these indicators are identified, a proper system to monitor and control their evolution over time is neglected. In this paper, a novel methodology to manage Energy Performance by developing, analyzing and maintaining Energy Performance Indicators in manufacturing plants is presented, which has been created to fill this gap and taking into account ISO 50001:2011 and ISO 50006:2014 requirements, in order to be as general and widely applicable as possible. The proposed methodology allows an immediate identification of manufacturing plant's energy performance deviations through Energy Performance Indicators monitoring over time and an easy association of causes and responsibilities to such deviations, in order to allow companies to rapidly react defining action plans and resetting targets if necessary; it has also been validated through an industrial case study, which will eventually be presented.

PurposeThe purpose of this paper is to provide a method for planning and controlling energy budgets for an industrial plant. The developed method aims to obtain a very high confidence of predicted electrical energy cost to include into the estimation of budget and a continuous control of energy consumption and cost.Design/methodology/approachThe authors propose a methodology that refines effectiveness and efficiency of budget estimation. The method relies on a three‐stage analysis: energy consumption characterization and forecasting, energy budget formulation and energy budget control. In particular, this paper deals deeply with the second and the third stages, i.e. energy budgeting and control. The methodology has been developed on the basis of a continuous improvement philosophy and project management techniques. A discussed case study shows the potential of the methodology in order to discover energy consumption inefficiencies.FindingsEnergy budgeting and control has been implemented within a set of first and second level metrics. The first level indicators allow identifying the effect of an increase of specific consumption beyond the predicted. The second level indicators allow identifying the effect of variations of price, volume, mix or loading bands from the predicted.Research limitations/implicationsIn the paper climatic variations are not considered, limiting the energy drivers to those related to production volumes.Practical implicationsThe method can be considered as a practical guide for energy budget planning and control of any industrial consumer.Originality/valueA new approach to energy budgeting and control is proposed, allowing the impact of different specific consumption or production plans (volume, mix, and load bands) to be calculated.