Available online at www.sciencedirect.com
ScienceDirect Procedia CIRP 61 (2017) 376 – 381
The 24th CIRP Conference on Life Cycle Engineering
Internet-of-Things Enabled Real-Time Monitoring of Energy Efficiency on Manufacturing Shop Floors Yee Shee Tana,* , Yen Ting Nga, Jonathan Sze Choong Lowa a
Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, Singapore 138634
* Corresponding author. Tel.: +65 6319 4451; fax: +65 6250 3659. E-mail address:
[email protected]
Abstract Energy efficiency (EE) has become an important indicator in manufacturing industry due the rising concerns about climate change and tightening of environmental regulations. However, most manufacturing companies today are only able to monitor aggregated energy consumption and lack the real-time visibility of EE on the shop floors. The ability to access energy information and effectively analyze such real-time data to extract key indicators is a crucial factor for successful energy management. Therefore, in this paper, we introduce an internetof-things (IoT) enabled software application for real-time monitoring of EE on manufacturing shop floors. While enabling real-time monitoring of EE, it also applies data envelopment analysis (DEA) technique to detect abnormal energy consumption patterns and quantify energy efficiency gaps. Through a case study of a microfluidic device manufacturing line, we demonstrate how the application can assist energy managers in embedding best energy management practices in their day-to-day operations and improve EE by eliminating possible energy wastages on manufacturing shop floors. © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2017 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 24th CIRP Conference on Life Cycle Engineering. Peer-review under responsibility of the scientific committee of the 24th CIRP Conference on Life Cycle Engineering Keywords: Energy Efficiency; Real-Time Monitoring; Data Envelopment Analysis; Best Practices; Internet-of-Things
1. Introduction Nowadays, efficient usage of energy is becoming more of priority due to the rising concerns about climate change and regulatory requirements. Among all end-user sectors that are targeted for achieving energy efficiency (EE) improvement, manufacturing industry is deemed one of the high potentials as it is the largest consumers among all end-user sectors. According to Singapore’s energy statistics, the energy consumption by industry sector was 42.6% of total energy consumed, followed by the commercial and household sectors, which are 36.5% and 14.9%, respectively in 2015 [1]. To improve EE, the role of energy management has greatly expanded in manufacturing industry as it has also resulted in reducing operating costs in long term. As defined by ISO 50001:2011, energy management is a comprehensive and systematic approach for energy conservation efforts in an industry. It is judicious and effective use of energy to
maximize profits and to enhance competitive positions through industrial measures and optimization of EE in the process. For energy management, the first step is usually the energy monitoring. Industry can only manage their energy when they initiate to measure and understand their current energy performance. However, industry today is only able to monitor aggregated energy consumption, but unable to visualize real-time EE at shop floor. The ability to access energy information and effectively analyze such real-time data to extract key indicators is a crucial factor for successful energy management. The new emerging technology, Internet-of-Things (IoT), which connects physical objects using electronic sensors and internet is drawing attention nowadays. IoT technology promotes the heightened level of awareness about the world and a platform from which to monitor changing conditions and react to those changes [2]. IoT is expanding to many other interesting application domains while energy is one of the
2212-8271 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 24th CIRP Conference on Life Cycle Engineering doi:10.1016/j.procir.2016.11.242
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2.1. Concept Overview The proposed IoT enabled software application helps energy managers in achieving better EE by understanding both the energy consumption patterns and production data while eliminating possible energy wastages in the manufacturing operation. It works in a simple manner as shown in Fig. 1. The application users, e.g. energy manager, monitor the energy performance for each machine at shop floor using tablet while data such as power consumption and process operating parameters, e.g. temperature, pressure, etc. are captured via sensor or controller. Production data are provided by existing software such as manufacturing execution system, work order tracking system, etc. The server stores the data as well as the energy performance status and respective analysis results in the common repository. The three components, i.e. data acquisition, server and energy manager, interact with each other via wireless network. Shop Floor
Software Architecture Data Capturing via Sensors/Meters
Standard Data
• Energy consumption • Waste generation
Data Capturing via Controller • • • •
Real-time EE monitoring
Material input rate Throughput Power consumption Process condition
• Product type • Throughput
WLAN
WLAN
DAQ node
LAN WLAN
Common Repository
WLAN
Server
Fig. 1. Concept overview of Internet-of-Things enabled software application
A software application is developed and structured as a multi-layered application consisting of user experience, business logic and data layers [9]. As Fig. 2 illustrates, it consists of (1) Presentation layer This layer contains the components that implement and display the user interface and manage user interaction. A set of user interface components such as the dashboard, notification pages and reports are design to provide a way for users to interact with the application. User interface can make use of controllers to communicate with the back-end and to navigate or process the interface components.
User
Presentation Layer
Mobile Application UI Components - Energy efficiency dashboard - Alert notification
Cross-Cutting
Security
Business Logic Layer
2. Internet-of-Things enabled software application for real-time energy efficiency monitoring
2.2. Software application architecture
Data Layer
application areas where IoT technology plays a major role [3]. Energy management is integrated with IoT technology to provide the ideal solution for monitoring real-time energy consumption while providing the level of awareness of energy performance [4][5]. With the support of IoT technology, i.e. energy sensor, energy consumption data can be collected in real-time at different levels, such as machine, production line or facility level [6]. However, collection of these data without the production or operating data will not be sufficient to understand EE [7][8]. Thus, this paper aims to bridge the gap by introducing an approach that uses both the energy and production data for EE assessment. Coupled with the IoT technology, the approach is able to provide real-time EE monitoring in manufacturing shop floors. Besides, data envelopment analysis (DEA) technique is applied to identify abnormal energy consumption patterns and quantify energy efficiency gaps. Through a case study of a microfluidic device manufacturing line, we demonstrate how the application can assist energy managers in embedding best energy management practices in their day-today operations and improve EE by eliminating possible energy wastages on manufacturing shop floors.
Monitoring Engine
Benchmarking Engine
Data Access Components
Service Agents
Configuration
Communication/ Connectivity
Database
Fig. 2. Software application structure for real-time EE monitoring
(2) Business logic layer This is the layer where all the engines in the application reside. It contains all the processing logic to make the application possible. The application consists of two parts, i.e. a) monitoring algorithm and b) benchmarking engine. a) Monitoring algorithm Monitoring is a process for metering the energy consumption and collecting real-time energy data. Data collected from monitoring is served as a basis to understand current level of energy use. With the basis, we can identify patterns and obtain information that can further be used to implement corrective and preventive action. However, as mentioned, in order to improve EE, integrating energy and production data is important. The algorithm flow chart to show how we correlate energy and production data is presented as Fig. 3. Inputs considered are 1) power consumption data (P) captured via sensor or controller and 2) production data (PV) tracked by production monitoring software, such as work order tracking system. The interval data (i) comes in increments of 1-minute granularity. With the acquired data, the energy consumption and production volume involved with respect to different reporting period r,
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i.e hourly, daily, weekly and monthly are evaluated. By correlating the evaluated energy consumption (Er) to the key drivers such as the production volume (PVr) at respective period, we can then identify the machine status as “no operation”, “idling”, or “operating”. While if the machine is operating, specific energy consumption, i.e. energy consumption per production volume, will then be calculated to understand how effectively energy is used to produce given amount of production or to deliver certain work by the machine.
considered as historical best. By comparing each DMU with the historical best practice, the relative efficiency is evaluated by dividing the respective Er of the historical best and evaluated DMUs. For instance, DMU P2 which is one of the historical best is the efficiency reference set for DMU P6 as well as P2 is performing better than P6 as it is consuming less energy Er while producing the same output, PVr. Thus relative efficiency for P2 is evaluated as P6(Er)/ P2(Er) while relative efficiency for P6 will be 1 as it is the historical best.
P4 Er
Start
Sampling interval, i
Power consumption at time t, Pt
Calculate total energy consumption for different reporting period r
Production volume at time t, PVt
P8
P6 P7
P5
Calculate total production volume for different reporting period r
P3 P2
P
s
T
where s: start of reporting period T: end of reporting period t r: {hourly, daily, weekly, monthly}
Yes
P1 PVr
No
ǫ
Fig. 4. Illustrative example for DEA Yes
Show “No Operation” for entity status
where s: start of reporting period T: end of reporting period r: {hourly, daily, weekly, monthly}
Show “Idling” for entity status
ǫ
No
Calculate specific energy consumption for different reporting period r
End
Fig. 3. Flow chart for monitoring algorithm
b) Benchmarking engine Benchmarking is the next process taken into consideration after the understanding of current energy performance via monitoring. Benchmarking is a process of searching for those practices which lead to the excellent performance. This help to establish baseline, and hence highlight the problem area as well as the potential for improvement in comparing with the best practices. DEA, which is a very powerful benchmarking technique is selected and applied. It is a non-parametric method for evaluating relative efficiency of decision making units (DMU) based on multiple inputs and outputs. An input oriented Banker, Charnes and Cooper (BCC) DEA models that assumes variable returns to scale while minimizing inputs (i.e. energy consumption) and keeping outputs (i.e. production volume) at current levels are considered [10]. DMUs defined in this study have the granularity of time and spatiality, for instance DMU is the hourly energy (Er) and production (PVr) performances. As a result from linear programming, which used to construct a non-parametric piece-wise surface over the data, by minimizing inputs Er and keeping outputs PVr at current levels (equation in Fig. 5), those DMUs that lie on the envelope (e.g. P1, P2, P3 and P4 in Fig. 5) are identified and
The obtained relative efficiency is then clustered into three categories using quantile classification. Three categories are selected for showing the alerts based on the red, amber and green colours used in a traffic light rating system. Top 25% of DMUs (ˁ>Q3) are considered as good as historical best, showing the machines with “normal” energy performance. While the lowest 25% (ˁ
