Mitsubishi Heavy Industries Technical Review Vol. 59 No. 2 (June 2022)
1AI Remote Monitoring and Operational Support SystemMaiDAS® for the Sustainability of Waste to Energy Plants
YOSHINORI TERASAWA*1 WATARU SUZUKI*1
TOMOMICHI EGUSA*2

KEIICHI HAYASHI*3YUTARO YAMAOKA*4TOSHIHIKO SETOGUCHI*5

Mitsubishi Heavy Industries Environmental & Chemical Engineering Co., Ltd. (MHIEC) has
developed MaiDAS®, which is a next-generation AI-based remote monitoring and operational
support system for Waste to Energy (WtE) plants. This system enables advanced automatic
operation while maintaining the health of important equipment for plant operation and optimizing
the amount of evaporation and the output of exhaust gas concentration while eliminating the effectof variable factors associated with individual operators. The use of this system can predict in realtime values such as Low Heating Value (LHV), the amount of waste feed, and combustion
conditions. The advanced automatic operation, which is controlled by the system, can considerably
reduce manual intervention. The stabilization of the main steam flow rate has also been confirmed.
Together with this system, the improvement of the already-developed waste pit mixing and feeding
support system is considered to make it possible to realize both stable operation and cost
reduction, while maximizing the sustainability of WtE plants.

|1. Introduction
In recent years, one of the most important needs of WtE plants is the simultaneous realizationof stable operation and cost reduction, while maximizing their sustainability. Furthermore, remote
operational support is getting more indispensable than ever, when stable operation and cost
reduction are also aimed to be achieved in relation to the shortage of experienced operators, thespread of infectious disease, and CO2 emissions reduction to curb global warming. Given thesecircumstances, MHIEC has developed MaiDAS®, an AI-based remote monitoring and operational
support system for the optimal management of WtE plants. This system enables advanced andefficient plant operation by aggregating the operational data from each plant using the cloud serverand sharing the know-how obtained therefrom with other plants, with a view to centrally managing
the operating conditions of multiple plants(1)(2). For better combustion stability, we are augmenting
the already-developed waste pit mixing and feeding support system(3) with a crane automation
system. This report presents the results we have obtained for these systems.

|2. System overview
The remote monitoring and operational support system currently in operation was first
introduced when we delivered a gasification and ash melting treatment facility in 2005, andafterward, it was installed mainly in the Design-Build-Operate (DBO) WtE plants. At this moment,
we are operating the system in conjunction with eight plants across Japan. Since FY 2018, we havebeen working on the upgrade of the entire system, utilizing AI models implemented in the secure
cloud server where we have built the MaiDAS® next-generation remote monitoring and operational
support system (Figure 1). It is a fusion of AI technologies such as machine learning (especiallydeep learning) and network technologies such as IoT. As shown in Figure 2, MaiDAS® offers
subsystem options to choose depending on the needs of each customer rather than always providing*1

Chief Staff Manager, Digitalization & Development Unit, Mitsubishi Heavy Industries Environmental & Chemical Engineering Co.,

*2

Group Manager, Digitalization & Development Unit, Mitsubishi Heavy Industries Environmental & Chemical Engineering Co., Ltd.

*3Group Manager, Special Purpose Company Management Promotion Unit, Mitsubishi Heavy Industries Environmental & Chemical*4Design Unit, Mitsubishi Heavy Industries Environmental & Chemical Engineering Co., Ltd.

*5Senior Researcher, Combustion Research Department, Research & Innovation Center, Mitsubishi Heavy Industries, Ltd.

Ltd.

Engineering Co., Ltd.

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the whole set, while securing the commonality among the subsystems. The following chapters will
present its main subsystems: MaiDAS® Dashboard, MaiDAS® Visualizer, and MaiDAS®
Navigator. With these subsystems, we aim at advanced automatic operation that can almost
eliminate the need for manual intervention. MaiDAS® is a registered trademark of MHIEC in Japan(Trademark Registration No. 6333918).

Figure 1 Configuration of MaiDAS® AI remote monitoring and operational support system
MaiDAS®

Figure 2

Positioning of MaiDAS® subsystems|3. MaiDAS® Dashboard and MaiDAS® Visualizer
Figure 3 is a conceptual diagram of advanced automatic operation with the system. In thisoperation, two approaches are taken: “stabilization” and “automation”. Either approach has twoelements: “prediction of the main steam flow rate, etc., to accurately grasp the situation and takeproactive measures” and “automatic detection of unexpected events” for the former, and“operational guidance to avoid delay in response” and “operational scoring to assess quantitatively”
for the latter. Moreover, advanced automation to be realized by the system can be tailored toindividual sites by linking the on-site subsystem (MaiDAS® Navigator, to be described later) andthe one in the cloud server (MaiDAS® Dashboard) accordingly. With the use of the operational dataobtained through the system and our support know-how, we aim to realize the advanced automatic
operation of WtE plants (i.e., automatic operation with maximized performance while considering
the service life of equipment) and operation management that is independent of variable factors

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associated with individual operators (i.e., consistent high-quality operation and maintenance thatanyone can safely perform without errors). The data obtained on-site are visualized and analyzed inreal time by MaiDAS® Visualizer, while MaiDAS® Dashboard (Figure 4) builds a model based onthese data and provides three scores regarding the operating conditions (operational scoring). The
three scores represent (1) operation, (2) output and (3) equipment, respectively. MaiDAS®
Dashboard can also display the predicted amount of evaporation and automatically detect theoccurrence of an unexpected event (such as feeding of large amounts of waste). A concept ofoperational scoring is shown in Figure 5, in which the integrity of the execution of operation isindicated by (1) operation score and (2) output score; the amount of evaporation and the output ofexhaust gas concentration (3) equipment score; the health of important equipment for plant
operation such as fans. The goal is to maintain operational stability by assessing the operating
conditions while keeping a balance between these scores.

Figure 3

Concept of advanced automatic operation

Figure 4

MaiDAS® Dashboard

With these subsystems, machine learning-based prediction of process data (e.g., main steam
flow rate, and heating value of waste) becomes possible, whereby the operator can be informed of
the situation a few minutes in advance. The deep learning-based anomaly detection models are used
to promptly detect events such as anomalous feeding of waste. A prediction example of the main
steam flow rate is shown in Figure 6. As these models are kept in the cloud server, their upgrading
and maintenance can be performed while the incinerator is in operation. By checking suchoperational guidance with the results of actual operations based thereon, the guidance model iscontinuously improved. In this way, it becomes possible to realize advanced automatic operation ofMitsubishi Heavy Industries Technical Review Vol. 59 No. 2 (June 2022)
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WtE plants and their efficient operation and management, while securing both operational stability
and economic performance with maximized sustainability.

Figure 5

Concept of operational scoring

Figure 6

Prediction of the main steam flow rate by MaiDAS® Visualizer|4. MaiDAS® Navigator
In order to carry out stable waste treatment and power generation at WtE plants, it isnecessary to maintain stable combustion despite varying properties of waste or varying amounts ofwaste fed to the combustion zone. Usually, the automatic combustion control (ACC) in use
achieves stable power generation by waste combustion. When the waste properties or fluctuationsof waste feed amounts go outside the ACC range, experienced operators can manually handle thesituation to retain stable plant operation. The newly developed MaiDAS® Navigator is, as shown in
Figure 7, a combustion control and operational support subsystem by which such manual handlingcan be automated based on the waste quality and waste feeding conditions indicated by special
measurement data (i.e., waste water content, waste layer image, and combustion flame image) andplant data, coupled with the advanced distributed control system (DCS). For combustion
stabilization, the waste feeding and combustion conditions are assessed in a quick and appropriatemanner, and are controlled in accordance with the assessment results. It is possible for the operator
to grasp the operating conditions at a glance during automatic operation. Further linking with theMaiDAS® system via the cloud server can improve the accuracy of advanced automatic operation.

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5Figure 7

Configuration of MaiDAS® Navigator

The special measurement results of MaiDAS ® Navigator are given in Figure 8. Figure 8-1
shows a moisture meter attached to the side of the hopper chute, whereby the water content andLHV of waste passing through the hopper chute can be measured based on the attenuation rate ofelectromagnetic waves to which the passing waste is exposed. Figure 8-2 shows images of waste
layers on the feeder, based on which the scale of the falling of waste from the feeder is estimated topredict the amount of waste to be fed. For the utilization of combustion flame images, machinelearning is used to extract features from the images. The images features are then related to thecorresponding plant data and combustion conditions through machine learning, whereby thecombustion conditions can be predicted in real time. The prediction results are given in Figure 8-3.
As shown in Figure 8-4, the interrelationship of the obtained data (i.e., LHV in the hopper,
combustion conditions data, plant data, combustion flame image, waste layer image, and main
steam flow rate) is to be machine-learned, so that the main steam flow rate can be predicted in realtime. The advanced automatic operation in which these predicted values are added to correct thecontrol variables can reduce the manual intervention by 90%, as shown in Figure 9. The
stabilization of main steam flow rate has also been confirmed.

Mitsubishi Heavy Industries Technical Review Vol. 59 No. 2 (June 2022)
6Figure 8-1

Figure 8-3

Figure 8

Prediction of the water content and LHV
in the hopper chute

Figure 8-2

Prediction of the amount of waste feed

Prediction of combustion conditions

Figure 8-4

Prediction of the main steam flow rateSpecial measurement results

Figure 9 Stabilization of the main steam flow rate with advanced
automatic operation

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7|5. Improvement of the waste pit mixing and feeding support system
In developing this system, we first analyzed how cranes were operated in order to visualize
the work load over time, and made a request to complete the questionnaires about waste crane
operation. Using the results, the waste mixing conditions were numerically evaluated from the threeperspectives of “the number of mixings”, “apparent specific gravity” and “retention time”. Each of
the three was then weighted. We thus developed a method to assess the mixing conditions using, as
an indicator, the comprehensive evaluation point (= K1 × the number-of-mixing evaluation point +K2 × the apparent-specific-gravity evaluation point + K3 × the retention-time evaluation point) (3).
Figure 10 shows the waste pit mixing and feeding support system. The logic was built for craneautomatic operation in accordance with the waste being received, together with the logic forautomatic mixing based on the above-mentioned waste quality evaluation method using thefrequency of waste mixing. It has thus become possible for automatically operated cranes to receive
waste, mix the pit, and provide waste feed. Figure 11 shows the results of system verification. It
indicates smaller LHV variation than the conventional operation, leading to the improved stabilityof waste quality. As a result, the accuracy of combustion control is improved, which in turnenhances the stability of advanced automatic operation.

Figure 10Waste pit mixing and feeding support system

Figure 11Results of system effect verification

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8|6. Conclusion
MaiDAS® is a next-generation AI-based remote monitoring and operational support systemdeveloped by MHI Group. It mainly consists of the three subsystems: MaiDAS® Dashboard that
performs operational scoring, MaiDAS® Visualizer for real-time visualization and analysis, andMaiDAS® Navigator enabling advanced automatic combustion control using AI. With the waste pit
mixing and feeding support system, which has been improved, the waste quality can be stabilized
and the accuracy of combustion control can be enhanced. As the application of these systems
enables advanced automatic operation that can eliminate the effect of variable factors associatedwith individual operators, we consider that the sustainability of WtE plants can be bettered in termsof plant management sophistication and efficiency in times of uncertainty.

References
(1)
(2)

(3)

Shu Pang et. al, Application of AI/IoT Technology in the Incinerator Facilities, Proceedings of The 32nd
Annual Conference of Japan Society of Material Cycles and Waste Management 2021, (2021-10) p.287
Nobuharu Iwashita et. al, Creating Customer Value Through Advanced Driving Automation Using
Remote Monitoring and Driving Support Systems, Proceedings of The 32nd Annual Conference of Japan
Society of Material Cycles and Waste Management 2021, (2021-10) p.265
Yoshinori Goto et. al, Development of a Waste Mixing and Feeding Support System, Proceedings ofThe 41nd Waste Management Research Symposium, (2020-1) p.212