Maturity-based approach for the improvement of energy efficiency in industrial compressed air production and use systems

Energy 186 (2019) 115879

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Maturity-based approach for the improvement of energy efficiency in industrial compressed air production and use systems
a, Ilaria Bertini a, Vito Introna b, Simone Salvatori c, Miriam Benedetti a, *, Francesca Bonfa Stefano Ubertini c, Rosanna Paradiso d a

Energy, New Technology and Environment Agency (ENEA), Via Anguillarese 301, 00123, Rome, Italy University of Rome “Tor Vergata”, Department of Enterprise Engineering, Via Del Politecnico 1, 00133, Rome, Italy University of Tuscia, Dept of Economics, Engineering, Society and Business Organization, Via Del Paradiso, 47, 01100, Viterbo, Italy d EURAC Research, Institute for Renewable Energy, Viale Druso 1, 39100, Bolzano, Italy b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 February 2019 Received in revised form 18 June 2019 Accepted 3 August 2019 Available online 5 August 2019

Compressed Air Systems (CASs) are one of the most critical utilities from an energy point of view, as their overall efficiency can be as low as 10e15% and their incidence is about 10% of the total energy consumption in manufacturing companies, and up to 25% in process industry. Therefore, improving their energy efficiency is crucial to achieve cleaner production objectives. Nevertheless, basing the search for efficiency improvements only on new technological solutions might not be sufficient in the long term to trigger continuous improvement mechanisms. The creation and diffusion of a Compressed Air Systems Energy Efficiency Maturity Model (CASEEMM) can help guiding companies in an improvement path that includes managerial and organizational enhancements. This paper presents the design of an effective CASEEMM, as well as the definition of a self-assessment tool. The presented CASEEMM is validated through implementation in three different industrial cases. Such cases have been chosen in order to test the tool in three different industrial sectors and with three different maturity levels, in order to evaluate both its effectiveness and general validity. The CASEEMM showed good potential in providing a clear and unbiased picture of industrial energy behavior. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Energy Efficiency Compressed air systems Maturity model Energy intensive industry

1. Introduction Compressed air is one of the most common forms of energy transfer in both civil engineering and industrial systems and plays a strategic role in achieving the compelling objective of reducing industrial energy consumption [1e6], due to its large utilization in industrial plants [7,8] and to its high energy intensity [7,9e15]. Despite the many advantages presented by the use of compressed air in industrial processes, CASs present the major shortcoming of a dramatically high energy intensity, being responsible for about 50% of the whole industrial electricity consumption [13,16e19]. In order to define a coherent path for energy efficiency (EE) improvement of CASs, companies should have a clear picture of the current status of the system as well as of new technologies available and best practices [20]. One of the most common ways to achieve it is energy auditing, a practice-oriented method to quantify energy

* Corresponding author. E-mail address: [email protected] (M. Benedetti). https://doi.org/10.1016/j.energy.2019.115879 0360-5442/© 2019 Elsevier Ltd. All rights reserved.

use, identify energy consumption reduction possibilities and plan actions to improve EE and reduce energy costs [10]. An energy audit can be useful to understand energy consumption but, in order to have a more complete picture, they have to be complemented with an evaluation of the investment risks and potential returns [21]. Thus, audits allow to get a first understanding of industrial energy management needs and practices and to define potential ways to increase energy performances, while do not guarantee an overall view of the issue nor, especially if the auditor is an external expert, knowledge transfers to the industrial company, as well as an integration of the EE improvement path identified with internal procedures and structures. Research has been done to create tools to support companies wishing to gain a more complete understanding of EE improvement processes and to integrate such processes in daily management routines. As a first step, the research was focused on tools suitable for CASs, in accordance with the area of interest. Successively, the research has been extended to tools dedicated to other facilities within industrial plants and to the whole plant. In this way, it was

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possible to familiarize with available tools and to verify potential cross contamination opportunities. Research results (briefly summarized in Fig. 1) show that several actions, often resulting in research programs and tools design, have been undertaken in order to help companies improving their energy performance through a correct implementation of energy management techniques. Such efforts highlight the still and always higher interest of researchers and practitioners for this topic. Most of the tools reported in Fig. 1 aim to evaluate and make suggestions on how to improve industrial energy performances through common best practices, and are based on energy audit methodologies. Some tools are designed for a whole plant assessment [22,34,35], while others are applied to a specific facility or topic [22e33]. Tools included in the first category generally allow sectorial benchmarking and include overall energy management practices. They require a fair amount of data and information as input and provide improvement opportunities as output. Nevertheless, such opportunities are usually sector-specific and not prioritized to indicate a specific development path. Tools included in the second category are instead more focused on technical improvements and provide benchmarking only when systems are assumed to be ideal. They require detailed information on the system (which is usually a sub-system of the production plant). In such context, the DOE - AIRMasterþ and ModSCA are tools

specifically tailored to CASs, while the Compressed Air System Best Practice program did not develop any specific tool. The first tool takes compressors specifications, load profiles and air demand in input and gives an overall assessment and improvement suggestions as output [22]. The second is a modular tool to simulate various aspect of the system. All these tools are mainly focused on technical assessment and do not consider any energy management practice nor behavioral challenges. The work presented in this paper is part of a larger project, which aims at assessing the potential for EE improvement of CASs in Italian large and energy intensive companies and at providing such companies with related support tools [16,36,37]. Main findings of the first phase of the project [16,36], mainly obtained through a survey and results comparison with existing databases [38], are related to the high relevance of consumption of CASs compared to the total energy consumption of the Italian industrial sector and the still low percentage of companies currently measuring and monitoring these data. In this context, technological improvements have to be based on managerial and organizational enhancements and on a better knowledge of energy consumption processes. Among other options, Maturity Models (MMs) [39] rather than energy audits, appear to be useful tools for enhancing EE of CASs, as they allow to identify organizational dimensions to be involved in the improvement

Fig. 1. Existing projects and tools aimed at communicating and transferring Energy Management practices to industrial companies.

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process, by selecting the most appropriate competence development paths and monitoring progresses. Thus, they not only allow to identify EE opportunities, but also to prioritize them and to develop specific knowledge and competences needed to actually implement and maintain them. Therefore, the second phase of this project, on which the present paper is focused, is aimed at the creation of a maturity model for EE of CASs, named CASEEMM (Compressed Air Systems Energy Efficiency Maturity Model). MMs are particularly suitable for knowledge transfer oriented activities. In fact, they allow to define a specific and tailored improvement path for a specific discipline basing on an assessment of current conditions and on a benchmark analysis against best available practices. They are often used in self-assessment modality, which enables practitioners and industrial users to identify major improvement areas as well as potential corrective/ improvement actions and to increase their awareness and knowledge regarding the state of the art in the specific discipline considered. This is all done in a simplified and systematic way that enhances engagement and effectiveness. A broad overview of the research design used for the development of the CASEEMM is given in Fig. 2. The following section of the paper gives an overview of the use of MMs for Industrial EE, providing the background of the work. A thorough literature review on main practices and tools for optimization of compressed air usage has also been conducted and constitutes a solid basis on which the CASEEMM has been created; since it is instrumental for the definition of the CASEEMM structure, it is reported in the methodology section rather than in the background section (see section 3.2 and Table 2). The rest of the paper describes the structure of the CASEEMM together with the self-assessment tool developed and tested.

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2. Background on Maturity Models for industrial energy efficiency MMs are management tools useful to monitor and improve process performances, generally used to indicate the capability of an organization regarding a specific domain [40e42]. A MM “allows individual and organizations to self-assess the maturity of various aspects of their processes against benchmarks” [43], and, at the same time, they provide “a systematic framework for carrying out benchmarking and performance improvement” [44]. Maturity increments can be represented in a continuous or staged scale: in the first case, the entity of each improvement is not predefined (i.e. improvements can be infinitesimal) while in the second case the level of maturity can only have fixed values on a predefined scale [45]. In addition, MMs can be characterised through the following three different features: number of characteristics, number of dimensions and number of levels [47]. A “characteristic” is a practice, an activity or a managerial aspect which is taken into account to evaluate the maturity level. Characteristics can be further grouped into “dimensions”, which can be defined as areas where performances are measured, as for example working conditions and measurement systems [41]. “Levels” are used in staged MMs and indicate the number of intermediate steps between the worst and the best performance. The optimal and most common number of levels is 5 [43,44,48]. MMs can be used in both external and self-assessment modalities, which means that the company can autonomously evaluate its maturity level and get useful information on how to improve it. The assessment can sometimes be supported by a questionnaire or similar tool to guide the assessment process and avoid biases and the results of the assessment can be given using different graphical tools (e.g. radar diagrams and a levels/dimensions matrix). MMs have been applied in several industrial areas, such as energy

Fig. 2. Broad overview of the research design used for the development of the CASEEMM, linking previous works.

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[15,39,45,47e53], manufacturing [54], risk management [55], knowledge management [56], Industry 4.0 [57,58], product lifecycle management [59]. The maturity of an organization in energy management is linked to its ability to manage the energy needs, from purchase or self-production of energy to its efficient use, based on the adoption of the best operating practices and on the use of energy efficient technologies [39,49]. Main features of MMs are can be conveniently defined on the basis of the classification proposed in Ref. [46] as in Fig. 3.

comprises four different steps (see Fig. 4): The first step has been recalled in the previous chapter, while the others will be illustrated in the followings. 3.1. Preliminary design of the model A preliminary design phase was conducted, defining potentially suitable characteristics and structure for the CASEEMM, on the basis of the review of previously developed Energy Management

Fig. 3. Main features of MMs as identified in Ref. [46].

We observe that existing Energy Management Models have a staged structure, which is easier to use and interpret and that they all share a method of analysis based on either best practices or main activities identification, seldom referring to a standard. On the other hand, different assessment methods are detectable: most of the models envisage self-assessment but provide a different kind of guide in order to make results more homogeneous and to avoid excessive biases, few models adopt non-guided self-assessment methods and other models just consider interviews conducted by experts. It is interesting to highlight that results often lack details regarding the development of maturity among different dimensions, being many of these models created to gather data for statistical/policy/research purposes, rather than improvement tools. In order to further support the statements here reported, a more precise description of MMs for Industrial EE and their main features for comparison is given in attachment to this paper as Supplementary Material. 3. Design methodology for the CASEEMM The methodology employed for the creation of the CASEEMM

MMs. This step is needed to perform a more focused best practices review (see next step), having a draft overall structure in mind. After the best practices review was completed, the draft structure was revised and validated. Two different sub-steps can be identified: (i) defining the main features of the model and (ii) defining the number and type of Levels and Dimensions. 3.1.1. Main features Features that were under discussion during the model preliminary design phase are the same presented as critical characteristics for all MMs in Refs. [39,46]. Such features, together with the option chosen for the CASEEMM and the rationale for the choice are presented in Table 1. 3.1.2. Structure The CASEEMM was mainly built on findings presented in Refs. [39,48,50] and in Ref. [49]. The first set of papers is referred to the general Energy Management of a production company and models present high compatibility with ISO 50001 requirements. Such models are structured so as to pave a path towards continuous improvement and the highest maturity involves not only BPs and

Fig. 4. Methodology employed for CASEEMM design.

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Table 1 Main features of the CASEEMM and rationale for choice. Feature

Chosen option

Rationale for choice

Maturity evaluation scale

Hybrid (different stages are defined, but the percent development of maturity dimensions is defined across levels)

Methodology of analysis

Comparison with identified best practices in each area

Reference to international standards

No explicit reference to international standards, but consistent with ISO 50001 sections

Method of assessment

Self-assessment through questionnaire

Results of assessment

Graphs for: synthetic indicator of maturity, coverage of levels of maturity (bar graph) and development of maturity along different dimensions (radar diagram)

Guide to improvement

A guide to improvement is attached to assessment results.

Literature reports benefits conveyed by a hybrid evaluation scale, which it is highly intuitive and easy to use as a staged scale [15,45,47e51,53] but also flexible and customizable as a continuous scale [50]. Since the CASEEMM is developed for being used autonomously and applied in different companies from different sectors and with different needs all these characteristics are needed. Many best practices for design, operations and maintenance of CASs are already available in literature. Thus, their identification is easier than the identification of other common objects of analysis, such as objectives or activities, which can be highly variable according to the industrial sector and kind of organization. The effectiveness of such option is also supported by previous works [15,39,47,50,51,53]. Avoiding explicit references to a specific standard allows not to bind the maturity evaluation of a single technical system (such as CASs) to a generalist set of requirements which are referred to the whole plant. Nevertheless, making dimensions consistent with the ISO 50001 allows users to adopt the MM as a support tool for managing CASs within the company Energy Management System. A similar choice was already made in literature with good results [39,48e51]. The choice of letting users assess their performances themselves has a threefold motivation: to make the model as easily and widely accessible as possible, to avoid biases introduced by external evaluators, and to enable a first rise in users' awareness during the assessment phase, starting to build the foundations of the development path depicted by model results. By answering the questionnaire questions and having a look at possible given answers, in fact, the user starts to autonomously question the as-is situation of its system and to identify options for improvement. This choice was undertaken considering the successful previous experience illustrated in Ref. [39]. The synthetic indicator allows to have one single value to be used for easy benchmarking with maturity of other companies and/or past values of maturity of the same company. The bar graph allows to have a clear picture of how the company has developed across different levels and to obtain a first information on how to improve (if lower levels are poorly developed then actions associated to such levels need to be undertaken, as they represent the foundations on which the company should develop), while the radar diagram gives information on which dimensions need higher efforts to be developed, suggesting areas of potential improvement. Such modality for presentation of assessment results has proven to be intuitive and effective in previous experiences [39]. Since the model is designed to be used autonomously by practitioners, attaching a guide to improvement allows to make assessment results even clearer and actually allows to return higher value to users, who can obtain precise information on how to improve their system. The guide is based on the comparison between assessment results and desired results expressed by users themselves, and is obtained by comparing current answers to questionnaire questions with answers needed to obtain desired results, and gives therefore a set of practices to be implemented. Attaching such guide to assessment results has proven to be valuable and effective in previous experiences [39].

BATs application but also the ability to keep pace with technological improvements. The rationale for this first choice related to the model structure is that the model is conceived as a continuous improvement tool, therefore sticking to state-of-the-art practices does not guarantee a company capability to pursue higher efficiency or to keep such efficiency over time. The second paper is instead the only work that focuses on a specific application (data centers) rather than addressing Energy Management in a generic organization. Therefore, technical contents in this model are organized in a way that is close to the one pursued by the authors for the CASEEMM, based on the identification of BPs and BATs for a specific system (i.e. data centers in Ref. [49], CASs in the present paper). The rationale for this second choice is very close to what reported regarding the choice of the method of assessment in the previous paragraph.

3.2. Best practices for energy efficiency of CASs A structured literature review of consolidated best practices for energy management of CASs was conducted to define the optimal development path (baseline) to evaluate the maturity of organizations (see Table 2). These best practices are very different one from another in nature (i.e. technical, managerial, information technology based, etc.), part of the compressed air and production systems involved, effort required, savings obtainable, etc., and their priority highly depends on what has already been done in terms of EE improvement and therefore on the maturity level of the company. The research also revealed that since compressed air is used for auxiliary operations and actuators more frequently than it is used for the production process itself, best practices tend to be rather similar across different industrial sectors. In addition, also

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measures that have a potential impact on energy costs rather than only on energy consumption are considered, as this topic has had a relevance in the international debate over the last years (see as e.g. Ref. [65]). Energy audits generally offer a stand-alone perspective of such EE measures, giving an evaluation based on costs and benefits of each of them without considering possible interactions or interrelated priorities. This second, more systemic view can be instead introduced by the MM.

interventions requiring a higher knowledge of the system and a deeper feasibility study, together with higher investment costs, are associated to higher levels of maturity. The definition of the threshold over which investments are considered too high is left to users, being it more related to a subjective and strategic evaluation of priority. Finally, to enable self-assessment modality, a questionnaire was designed and tested in different case studies. The full questionnaire is attached to this paper as Supplementary Material.

Table 2 Identified EE Best Practices for CASs: scientific literature is reported in bold, grey literature in underlined and white literature in italic. No

Best Practices

References

1 2 3 4 5 6 7 8 9 10

Creation of awareness campaigns on the importance of incrementing EE of CASs. Introduction of a systematic approach to EE opportunities identification. Accurate consumption/costs data acquisition (including data related to energy drivers). Air leak detection and prevention in the distribution network. Organization of technical trainings on EE improvement of CASs for all the staff. Definition of specific responsibilities for EE improvements of CASs. Elimination of inappropriate uses of compressed air. Performance of consumption/costs data analyses. Correct design of air intake. Implementation of activities to improve people's involvement in the identification of energy wastes related to CASs. Implementation of periodic energy audits focused on performances of CASs. Adoption of an energy consumption data management system. Correct design of machine's modulation (evaluation of inverter adoption). Organization of managerial trainings on EE of CASs for all the staff. Creation of a record management system for identified improvement opportunities. Adoption of an energy drivers data management system. Creation of appropriate maintenance schedules. Analysis of consumption and energy drivers data through statistical data analysis. Definition of optimal procedures for a careful system's design. Adoption of an internal communication system related to EE issues of CASs. Correct design of machine modulation (evaluation of automatic control system adoption). Implementation of EE ideas gathering and feedback/rewards mechanisms. Evaluation of the adoption of advanced technologies for system components. Compressed air demand and energy consumption forecasts performed. Reduction of pressure drops in the system. Adoption of an automatic system for the continuous control of performances of CASs. Minimization of air pressure required by the users. Definition and assignment of EE objectives related to CASs. Study and optimization of the system load profile. Scheduling of continuous training activities related to CASs EE for all the staff. Systematic evaluation of advanced technologies for EE of CASs (including waste heat and waste energy recovery innovative solutions). Optimization of IT systems for energy performance control. Definition of ad hoc procedures for keeping the system design optimized over time. Integration of all EE strategies for CASs into the company strategy.

[21,24,60]; [7,13,66,67] [13,21,60,62,66] [10,11,24,60]; [13,63,66,68,69]; [7,67] [9e11,21,24,60]; [13,63,66,68e73]; [7,61,67,74] [21,24,60]; [66,75] [60,66,75] [11,24,60]; [13,63,68,72,76]; [7,67] [24,60]; [13,63,66,68,69]; [7,67] [9e11,60,77]; [13,64,72,73]; [67,74] [13,24,60,66]

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

It is important to highlight that some best practices group several possible interventions, for sake of simplicity. For example, when referring to “system components careful design” authors intend to group all best practices related to all different technical components’ design. In addition, the adoption of certain technology is never considered a best practice per se, as the technical and economic feasibility of any measure (e.g. inverter or waste heat recovery system) highly depend on the specific system and boundary conditions, except for losses reductions (e.g. air leak detection), which are always profitable. Therefore, the best practices associated to the adoption of different and more efficient technologies is always considered to have already evaluated their feasibility, independently of the results of such analysis. 3.3. Final design of the model BPs selected through the literature review were analyzed and categorized according to dimensions and levels identified in the preliminary design phase. We highlight that those technical

[10,24,60]; [13,66,68]; [7,67] [11,24,60]; [13,66,67] [10,11,21,24,60,62,77,78]; [13,64,66,69,72,73]; [7,61,67] [21,24,60]; [13,66] [13,60,66] [13,24,60,66,67] [10,18,60,64,77e79]; [13,63,66,69,70,72]; [67,74] [13,60] [11,14,18,24,60,80e82]; [13,63,64,66,68,69,72,73,83]; [7,61,74] [13,60,66] [11,24,60,62,77e79]; [13,64,66,68,69,72,73]; [7,67,74] [60,61] [9e11,21,24,60,77]; [64,66,69,72,73]; [7,61,67] [13,60,83] [9e11,24,60]; [13,63,64,66,68e70,72,83]; [7,67,74] [7,13,60,66] [9,11,60,77]; [13,63,64,66,68,69,72,83]; [7,67,74] [13,60,66,67] [11,24,60,62,77]; [13,63,64,66,68,69,72,83]; [7,62] [13,21,60,66] [10,21,60,77,84]; [13,64,66,68,69,72,75]; [7,61,67,74] [10,60,62]; [13,66] [60,62]; [13,64,66,68,69,83] [13,60,66,85]

4. The CASEEMM for industry: main results The structure of the CASEEMM proposed in this paper is in stages with five maturity levels. For each level of maturity, we analyzed four dimensions: (i) awareness, knowledge and skills, (ii) methodological approach, (iii) energy performance management and (iv) BPs and BATs implementation. The overall structure is described in Fig. 5, while a more precise description of each level and dimension further detailed considering questions and answers provided in the questionnaire can be found as a Supplementary Material in attachment to this paper. A self-assessment questionnaire of 34 questions and a graphical report have been designed. These allow companies to autonomously assess their level of maturity and support them in improving their sensitivity and awareness, also reducing biases. The questionnaire is composed of a series of multiple choice questions with 5 possible alternatives for levels 2e4 and 2 possible alternatives for level 5. Different alternatives are structured so as if one of them is true, then also the previous ones are true (but only partially describe the current situation). In this way it is possible to

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Fig. 5. Evolution of maturity dimensions in the 5 maturity levels.

calculate the score obtained for each question as:

pi ¼

j1 ðn  1Þ

(1)

where pi is the score obtained for the question i, n are the possible answers, j is the chosen answer. Fig. 7 shows the attribution of different questions to different levels and dimensions. Results can be automatically processed and are presented using three different indicators: a synthetic indicator of maturity, the degree of coverage of levels, and the level of development of different dimensions. The synthetic indicator of maturity is a number between 1 and 5 and summarizes the maturity level of the organization in managing the EE of their CAS. It is calculated as follows:

Synthetic indicator of maturity ¼ 2*%2 þ

5 X

%n

(2)

analysis, i.e. into desired scores for each level and dimension. This “to be” scenario represents a good, structured basis to develop an improvement plan, thus allowing companies to implement not only short term measures (e.g. substitution of machine components), but also long term ones (e.g. creation of a performance monitoring system). The analysis of maturity reports allows identifying weaknesses and priorities for the development of an appropriate action plan, which should include the activities necessary to achieve the desired score for different dimensions and levels. A sequential development in maturity levels and a symmetric development in maturity dimensions would be ideally the best improvement path as it would allow to maximize results and minimize efforts, implementing always more complex (from a management and technical point of view) improvement opportunities. 5. First validation

n¼3

where %n is the degree of coverage of level n and. The degree of coverage of levels gives an indication on how the organization has reached the total score provided by the synthetic indicator of maturity. For each level, the degree of coverage is equal to the percentage of the score obtained compared to the maximum achievable score, evaluated only on the questions referring to the considered level. The degree of coverage of levels can be graphically represented as a bar graph, as shown in the left-hand side of Fig. 8. The development of dimensions provides information on how the growth towards maturity has been reached. A given value of maturity could in fact be achieved through a different development of various dimensions. For each dimension, the level of development is equal to the percentage of the score obtained compared to the maximum achievable score, evaluated only on the questions associated with the considered dimension and regardless of the level. The level of development of dimensions can be graphically represented by a radar diagram, as shown in the right-hand side of Fig. 8. The results of this assessment can be evaluated by companies in a strategical perspective. The analysis of the “as is” situation should result in a series of objectives to be implemented in the short and long term, to be translated into a “to be” scenario through gap

In order to validate the proposed structure for the CASEEMM as well as the effectiveness of the proposed methods for assessment and results representation, the model has been tested on three case studies. Companies have been chosen on the basis of their maturity level, as initially perceived by authors: one company was perceived as highly mature, one as quite mature and one as poorly mature. This allowed to test the effectiveness of the tool at different levels and to verify its ability to produce a realistic picture of the energy behavior. Selected companies also belong to different industrial sectors and are characterised by different sizes, market conditions and energy needs, in order to assess a general validity of the CASEEMM. In order to select the companies for validation, authors listed those involved in previous activities of the same larger project (see Refs. [36e39]) and then shortlisted those that had already demonstrated to be very interested in the topic, proactive, and highly available for data gathering and interviews. These aspects were considered fundamental for CASEEMM validation as discussion of the questionnaire results with the company is fundamental in order to validate results obtained in guided selfassessment. Among such shortlist, companies were finally selected so as to ensure diversity as illustrated at the beginning of this paragraph. During first applications of the CASEEMM, the questionnaire was always administered to utilities experts by the authors, who

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discussed the questions with them and set desired values together. Such procedure was adopted in order to identify an agreed development path, to ensure a correct interpretation of the questions proposed and to verify the suitability of the questionnaire for future use in self-assessment modality. The output of the questionnaire was validated also through comparison with energy audit outputs. Energy audits were previously conducted by third parties or internally within the companies, but did not involve the authors. This allowed to have a different point of view on EE needs. Such comparison allowed a first analysis of relevant differences among the MM approach and the energy audit approach, which is adopted by most available tools considered in Fig. 1. 5.1. Company 1: consumers’ goods producer The company perceived as the most mature produces consumer goods and, according to its total income and number of employees (i.e. about 911 million Euros turnover and 1400 employees), is classified as a Large Enterprise while, according to the total amount of primary energy consumed every year (i.e. around 13000 toe), it is classified as an energy-intensive company. It is a joint venture between an Italian company and a multinational group, which is a European leader in different sectors and with different brands, all related to personal and house care. The company is very focused on keeping high quality standards for its products and high security and comfort levels for its employees. Research and innovation is considered a critical factor, and 4% of total yearly incomes are reinvested in R&D. In addition, the company has demonstrated growing interest in sustainability over the last few decades, getting involved in several campaigns and awareness raising activities, both focused on the production process (and directed to company employees) and on the products (and directed to consumers). At the moment the questionnaire was completed, the company had already adopted management systems compliant with ISO 14001:2015, ISO 18001:2007 and ISO 9001:2015 and was developing an Energy Management System compliant with the international standard ISO 50001:2011, and a few months after it received the certification. The plant is composed of three different departments highly independent one from each other from a production management perspective (mainly because they produce very different types of products). The energy demand is almost completely satisfied by a centralized technical service, where most part of plant utilities are placed (in addition, there is some small local equipment). The plant is provided with a cogeneration system that is able to satisfy all its electricity needs (though the plant is still connected to the electricity grid). A schematic representation of the plant layout is given in Fig. 6. The score obtained by the company in each question is given in Fig. 7, which also reports the “desired” scores, which is to say those scores to be obtained in order to improve the current situation. These have been defined together with the Energy Management of the company according to the rules described in section 4 and to the EE objectives set by the company for the short-medium term. Whenever the desired score is different from the current one, it has been highlighted in yellow in Fig. 7. The value obtained for the synthetic indicator of maturity is then 2.97, while the desired value is 3.67. The graphs related to the degree of coverage of different levels (current and desired values) and to the development of dimensions (current and desired values) are given in Fig. 8. As it was easily foreseeable, the highest level is totally incomplete, as it represents the excellence in energy management of CASs. Nevertheless, the degree of coverage of other, lower levels is

Fig. 6. Schematic representation of the plant layout for company 1.

rather high, as is the perceived maturity level of the company. The balance among the degree of coverage of different levels and the symmetry of the different dimensions’ development is increased in the “to be” scenario (desired values). The current overdevelopment of Level 3 is probably due to the fact that the level is rather focused on the development of the third dimension (energy performance management) on which the company had worked hard in order to prepare the ISO 50001 audit. The underdevelopment of the fourth dimension (BPs and BATs implementation) is instead probably due to the fact that the company has only recently started to focus on efficiency improvement of CASs, as higher priority has been given in the past to other facilities (e.g. electric motors, heating and cooling systems). The improved balance and symmetry are then due to the planned implementation of training programs for the staff and to the adoption of new BPs and BATs, as, in particular improved detection and prevention of air leaks, better positioning of air intakes, adoption of an automatic control system for air compressors, reduction of pressure drops and of working pressure. The MM gave in this case similar results compared to the previously conducted energy audit. Nevertheless, the audit did not allow to prioritize interventions according to any criterion other than financial, while in this case the energy manager received support in better understanding the interconnections existing among different measures and also the impact of “soft” interventions on the whole system.

5.2. Company 2: foundry The company perceived as quite mature is a foundry and is part of a larger group including a machinery producer. The production is devoted to components for both different industrial sectors (textile, automotive, woodworking machines, earthmoving machines, compressors, pumps and adaptors) and urban furniture market. Around 90% of the products are sold to third-party clients all over Europe. According to total income and number of employees, the considered foundry is categorized as a Small-Medium Enterprise whereas according to the total amount of primary energy consumed every year it is categorized as an energy-intensive company. At the time the questionnaire was completed, the Quality Management System and the Environmental Management System of the company were in compliance respectively with the standards ISO 9001:2008 and ISO 14001:2004. Concerning the Energy Management, actions to improve EE were planned and

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Fig. 7. Score obtained by company 1 in each question: current (on the left-hand side) and desired (on the right-hand side) situation. When the desired score is different from the current one, it has been highlighted in yellow.

Fig. 8. Degree of coverage of different levels (current and desired values, left-hand side) and development of dimensions (current and desired, right-hand side) for company 1.

implemented according to the production priorities and out of a devoted System. Different types of products are manufactured in the plant but the stages of the production process are basically the same and can be grouped into three main departments. The preparatory stages take place in department 1, melting and pouring are performed in department 2 and finishing, testing and shipping are in department 3. The three warehouses are functional to the three departments. The plant thermal energy demand (heating, hot sanitary water, process heat) is satisfied by heat generators placed near the

utilities, whereas the compressed air production is centralized. A schematic representation of the plant layout is given in Fig. 9. The effective and desired scores obtained by the company in each question are shown in Fig. 10. In this case, the desired scores have been set considering the company's short-medium term objectives and their feasibility. The value obtained for the synthetic indicator of maturity is then 1.53, while the desired value is 2.18. The graphs related to the degree of coverage of different levels and to the development of dimensions are reported in Fig. 11.

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Fig. 9. Schematic representation of the plant layout for company 2.

Also in this second case the highest level is completely blank. Levels 2, 3 and 4 are less than half-covered, being 39%, 44% and 31% respectively. The balance among the degree of coverage of different levels and the symmetry of the different dimensions’ development is increased in the “to be” scenario (desired values). The current overdevelopment of Level 3 is probably due to the fact that the level includes several BPs and BATs the company had already evaluated or implemented, even though it did not follow a precise improvement plan. This situation is also well represented by the asymmetry

in dimensions development. In fact, dimension 2 (Methodological approach) and dimension 4 (BPs and BATs implementation) are rather well developed, while dimension 1 (Awareness, knowledge and skills) and dimension 3 (Energy performance management) are not. This is typical for companies that have never developed an Energy Management System, but have been carrying on some Energy Management activities in conjunction with other Management Systems’ activities. The improved balance and symmetry are then mainly due to the creation of an adequate performance management systems (including data acquisition and analysis and benchmarking activities), and to the implementation of missing lower levels’ BATs (e.g. air leaks reduction, correct dimensioning and design of air compressors). The energy audit that had been conducted before the MM implementation was extremely focused on technological interventions (BPs and BATs implementation), almost completely neglecting behavioral and managerial issues. The MM gave in this case good and new insights on managerial and educational needs. 5.3. Company 3: machinery producer The company perceived as the less mature produces machinery. It is part of a multinational group recognized as the global leader of manufacturing and distribution of engine parts and services for automobiles and other means of transportation. According to total income and number of employees, the considered company is categorized as a Large Enterprise. Different types of products are manufactured in the plant and the processes can be grouped in two departments. The production is carried out in batch mode and the products can be stored in warehouses before being sold.

Fig. 10. Score obtained by company 2 in each question: current (on the left-hand side) and desired (on the right-hand side). When the desired score is different from the current one, it has been highlighted in yellow. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

M. Benedetti et al. / Energy 186 (2019) 115879

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Fig. 11. Degree of coverage of different levels (current and desired values, left-hand side) and development of dimensions (current and desired, right-hand side) for company 2.

At the time the questionnaire was completed, the Energy Management System was still not compliant to ISO 50001:2011, but the company had started working on that to obtain the certification and had already implemented some EE improvement opportunities. The analyzed plant is constituted by two different departments, independent one from the other and located in two different buildings. A schematic representation of the plant layout is given in Fig. 12. The plant energy demand consists of electric energy and natural gas. Electric energy is used to produce compressed air and cooling energy. Compressed air is produced in a centralized unit working at two different pressure levels and is connected to the electricity grid for electric energy supply. Cooling energy is required for production activities (cooling water) and air conditioning. Within the plant five cooling units (chillers) grouped in two areas are used. Natural gas is used in thermal energy generation. The steam demand is satisfied by a centralized thermal unit, which contains a boiler for steam production and a boiler for hot water production. Energy performance are monitored in real time only for those utilities whose energy consumption is significant. Other utilities (those that consume less energy) are controlled with lower sampling rates. Nevertheless, a clear picture of the CAS's energy performance was still missing as well as a clear improvement plan. The results of the completed questionnaire are summarized in Fig. 13. The value obtained for the synthetic indicator of maturity is then 1.33, while the desired value is 1.96.

The graphs related to the degree of coverage of different levels and to the development of dimensions are given in Fig. 14. Also in this case the highest level is completely blank. Level 2 is almost half-covered (44%), while the coverage of Level 3 (31%) and Level 4 (13%) is lower, as expected considering the low level of maturity perceived. The balance among the degree of coverage of different levels and the symmetry of the different dimensions’ development is increased in the “to be” scenario (desired values). The current development of Level 3 is probably due to the fact that the level includes several BPs and BATs the company had already evaluated or implemented, even though it did not follow a precise improvement plan. This situation is also reflected by the underdevelopment of the second and third dimensions (Methodological approach and Energy performance management), clearly indicating the lack of a general overview of the systems’ performances. This could lead to a situation where the company is not able to get the most out of the implemented BPs and BATs and of training activities due to inappropriate control and management strategies. The improved balance and symmetry are then due to the systematization of the search for EE opportunities (e.g. through the institution of periodic diagnoses of the system and of a related opportunities database, and also though energy management trainings), to the creation of an adequate performance management systems, including data acquisition and analysis and benchmarking activities, and to the implementation of missing lower levels BATs (e.g. air leaks reduction, adoption of inverter). The MM gave in this case more complete results than the previously conducted energy audit. In fact, all interventions suggested in the audit were also included in the output of the MM as shortterm measures, while the latter also allowed to have a clear idea on issues to be tackled in the medium and long term, combining knowledge and competences development with new technologies adoption. The energy performance management aspect was completely ignored in the audit, while emerged as a key issue in the MM.

5.4. Discussion and comparison among the three companies involved in validation

Fig. 12. Schematic representation of the plant layout for company 3.

In order to facilitate discussion on tests results, a comparison among the three companies was conducted and main points are summarized in Table 3. Results of the tests were discussed within a group of academic and non-academic experts and representatives of the involved

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Fig. 13. Score obtained by company 3 in each question: current (on the left-hand side) and desired (on the right-hand side). When the desired score is different from the current one, it has been highlighted in yellow. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 14. Degree of coverage of different levels (current and desired values, left-hand side) and development of dimensions (current and desired, right-hand side) for company 3.

Table 3 Comparison among the three companies involved in validation. Company Maturity level as initially perceived

Industrial sector

Dimension

Energy intensity

Onsite energy production systems

Company High 1 Company Medium 2 Company Low 3

Consumers goods Large Intensive Cogeneration production Enterprise system Foundry Small-Medium Intensive None Enterprise Machinery Large Intensive None production Enterprise

Local/multinational ownership

Market Management systems in Synthetic conditions use indicator

Joint venture local/ Global multinational leader Multinational National leader Local European leader

Quality, Safety, Energy, 2.97 Environmental Quality, Environmental 1.53 Quality

1.33

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companies. Such discussions confirmed that the developed tool seems a suitable support to assess companies’ energy behavior and to define tailored improvement paths. The CASEEMM showed good potential in providing a clear and unbiased picture of companies’ energy behavior. For example, in the case of Company 2 the overdevelopment of Level 3 (coverage level higher than Level 2) gave the perception of a much higher level of maturity compared to Company 3 (where Level 2 has a higher coverage level than Level 3). Nevertheless, a deeper analysis confirmed what suggested by the CASEEMM, which is to say that the maturity level of companies 2 and 3 are almost the same The only relevant difference lies in fact in the EE improvement path undertaken by the two companies. Also, the degree of coverage of different levels and the symmetrical development of maturity dimension highly influence the design and final structure of the development path. In fact, the difference among the current and desired value of the synthetic indicator of maturity of Company 2 (more mature, but less balanced) is higher than that of Company 3 (less mature, but more balanced). This gives a useful measure of the efforts needed to improve EE of CASs and to keep it high over time. Furthermore, it also gives a clear indication of how the detailed analysis of the situation per level and per dimension is able to provide useful insights and should be always considered in evaluating the overall maturity level. The comparison of obtained results with previously conducted energy audits gave interesting insights in all cases, confirming the initial idea according to which the MM is able to allow prioritization, to highlight interactions among technological and managerial measures and also to better define and value non-technological interventions. In addition, in all cases the MM was perceived as more useful to involve the energy management team and operators in analyzing and defining EE and potential efficiency measures, thus obtaining higher staff involvement and also producing actual knowledge within the company. The need and will to increase internal knowledge on processes and EE within the company seems to be the most important criterion in deciding whether to choose an energy audit approach or a MM approach. 6. Conclusions and future developments This paper presents a novel MM designed to help companies assessing and improving the EE of their CASs. The structure of the model, named CASEEMM, together with a self-assessment tool, allows autonomous evaluation, enhances awareness raising and enables the identification of energy management opportunities based on managerial rather than on technological considerations. This feature is often missing in available tools, especially when focusing on specific technical systems. The proposed model provides support in defining a tailored improvement path towards higher maturity levels. The proposed model has been validated in three industrial cases, chosen considering their different perceived maturity level and their different industrial sector, so as to understand the ability of the model to correctly represent different situations. Next steps of this research will be to continue testing the model in different situations and companies in order to further prove its validity. It could also be useful to continue monitoring the three companies here presented in order to follow them in their improvement path, and to understand whether the improvement plan developed on the basis of the model results will actually be implemented and how. In addition, the questionnaire and the model itself will be also used to survey the energy maturity of defined industrial sectors and subsequently develop sector-specific guidelines to improve each of the dimensions considered in the CASEEMM. Analysis and decision support tools as well as implementation guidelines associated to each BP and BAT included in the

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questionnaire are under development. This activity is meant to provide a more practical support for companies willing to improve the efficiency and effectiveness of BPs and BATs planning and implementation. Finally, potential updates of the model considering technology developments should also be foreseen in the future. Acknowledgements This work is part of the Electrical System Research, implemented under Program Agreements between the Italian Ministry for Economic Development and ENEA, CNR, and RSE S.p.A. Authors would also like to thank Dr Marco Cozzini from EURAC Research for the review and precious suggestions provided. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.energy.2019.115879. References [1] Benedetti M, Cesarotti V, Introna V. From energy targets setting to energyaware operations control and back: an advanced methodology for energy efficient manufacturing. J Clean Prod 2017;167:1518e33. https://doi.org/ 10.1016/j.jclepro.2016.09.213. [2] Benedetti M, Cesarotti V, Introna V, Serranti J. Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: proposal of a new methodology and case study. Appl Energy 2016;165:60e71. https://doi.org/10.1016/j.apenergy.2015.12.066. [3] Finnerty N, Sterling R, Contreras S, Coakley D, Keane MM. Defining corporate energy policy and strategy to achieve carbon emissions reduction targets via energy management in non-energy intensive multi-site manufacturing organisations. Energy 2018;151:913e29. https://doi.org/10.1016/ j.energy.2018.03.070. [4] Cesarotti V, Ciminelli V, Di Silvio B, Fedele T, Introna V. Energy budgeting and control for industrial plant through consumption analysis and monitoring. In: Proc. IASTED Int. Conf. Energy Power Syst.; 2007. p. 389e94.
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