Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
1AI Remote Monitoring andOperational Support System MaiDAS®
for Optimal Management of Waste to Energy Plants

Mitsubishi Heavy Industries
Environmental & ChemicalEngineering Co., Ltd.
Business Unit, Domestic Division+81-45-227-1301

The recent needs of Waste to Energy (WtE) plants are often the simultaneous realization ofstable operation and cost reduction. While an increasing number of WtE plants are now run by theprivate sector on DBO (Design-Build-Operate) basis as a result of administrative transitions frommunicipalities, as the shortage of experienced operators accelerates, it is becoming increasingly
important to secure not only the conventional on-site-basis operation/management, but also toprovide a remote support system. Against this background, Mitsubishi Heavy Industries
Environmental & Chemical Engineering Co., Ltd. (MHIEC) has developed the MaiDAS® AI-based
remote monitoring and operational support system for the optimal management of WtE plants,
which increases the sophistication and efficiency of plant administration by analyzing theoperational data from each plant and sharing the expertise obtained therefrom with other plants,
in order to unify the operational management of multiple plants.

|1. System overview
The remote monitoring and operational support system currently in use was first introduced
when we delivered a gasification and ash melting system in 2005 and has been installed mainly inDBO-based WtE plants. At the moment, we are operating the system in conjunction with eightplants across Japan. Since 2018, we have been working on upgrading the entire system, utilizing AI
models implemented in the secure cloud space where we store the system operational expertise
accumulated by MHIEC and Mitsubishi Heavy Industries, Ltd. (MHI), and construct our MaiDAS®
next-generation remote monitoring and operational support system (Figure 1), which is a fusion ofAI technologies—such as Machine Learning and especially Deep Learning—with network
technologies such as IoT. In MaiDAS®, as shown in Figure 2, we utilize a framework where wecan select some of the subsystems to provide rather than the whole set only, depending on theneeds of our customers, while securing the commonality of each subsystem. In the following
chapter, we will introduce some of the major subsystems, namely MaiDAS® Dashboard, MaiDAS®
Visualizer and MaiDAS® Navigator.

Mitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
2Figure 1MaiDAS® remote monitoring and operational support system

Figure 2

Positioning of subsystems|2. MaiDAS® Dashboard and MaiDAS® Visualizer
Figure 3 illustrates the concept of the advanced automatic operation. This concept consistsof “stabilization” and “automation”. The former consists of “Prediction of the main steam flow rate
and other elements to ensure an accurate understanding of the situation and taking the initiative”and “Automatic detection of unexpected events”, whereas the latter is based on “Operationalguidance for avoiding delays in treatment” and “Operational scoring for quantitative assessment”.
Furthermore, linking the on-site subsystem (MaiDAS® Navigator, described later) and the one in
the cloud (MaiDAS® Dashboard) achieves the different forms of advanced automation suitable forindividual sites. By utilizing the operational data obtained via the system and our expertise inoperational support, we aim to realize the advanced automatic operation of WtE plants, that is,
automatic operation where the performance is maximized taking into account the service life of theequipment, and a form of operational management independent of variable factors associated withindividual operators, by which anyone can perform high-quality and homogeneous operation andmaintenance safely without error. MaiDAS® Visualizer is used to visualize and analyze the dataobtained from the site in real time. MaiDAS® Dashboard (Figure 4) is a web application whichbuilds a model based on such data and displays a list of guidance options necessary for operationalsupport.

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Figure 3

Concept of advanced automatic operation

Figure 4

MaiDAS® Dashboard

These subsystems allow Machine Learning-based prediction of the processing data (mainsteam flow rate, calorific value of waste, etc.), informing the operator of the situation a few minutes
in advance, and the instantaneous detection of various abnormalities such as the irregular dumpingof waste, utilizing an abnormality detection model based on Deep Learning. Figure 5 demonstrates
a sample prediction of the main steam flow rate. In addition, the corresponding steps are indicatedas operational guidance, which is part of the framework to support operations in these systems. As
these models are kept in the cloud, their upgrading and maintenance are possible while theincinerator is in operation. Furthermore, by upgrading the guidance model based on across-validation of the operational guidance and the results of actual operations based thereon, theadvanced automatic operation of WtE plants and their efficient administration and operational
management could be simultaneously achieved, while also securing both stability and economicperformance.

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Figure 5

Prediction sample of the main steam flow rate with MaiDAS® Visualizer|3. MaiDAS® Navigator
In order to carry out stable waste treatment and power generation at WtE plants, it isnecessary to maintain a stable combustion state regardless of variables in the waste properties suchas season, where the waste was collected, how it was stirred in the pit, etc., or even when theamount of waste sent to the combustion zone fluctuates. The existing automatic combustion system
has achieved stable power generation by waste combustion. Even when the waste properties areoutside the range that automation can handle or when the amount of waste fluctuates, manual
operation by experienced operators would still achieve stable facility operations. The
newly-developed MaiDAS® Navigator is, as shown in Figure 6, a combustion control andoperational support system where control by DCS (Distributed Control System) is advanced andthe manual operation is automated, utilizing image diagnostic technologies. In order to stabilize thecombustion state, the waste supply and combustion states are evaluated quickly and properly andcontrolled accordingly. It is possible for the operator to understand the current operating conditions
at a glance even during automatic operation. Furthermore, even more advanced automatic operationis possible by linking with the MaiDAS® system via the cloud.

Figure 6

Combustion control/operational support subsystem MaiDAS® NavigatorMitsubishi Heavy Industries Technical Review Vol. 58 No. 1 (March 2021)
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Figure 7 shows the data from a 3-week continuous run with the existing control combinedwith MaiDAS® Navigator. By identifying the waste supply pattern and properly controlling theprimary and secondary air flow rate and other relevant factors, we can see that stable operation wasachieved without manual operation, where the variations in steam flow rate were properly
controlled as shown in Figure 8.

Figure 7

Assessment sample of waste supply pattern utilizing combustion images

Figure 8

Stabilizing the main steam flow rate with MaiDAS® Navigator|4. Further development
In the future, aiming to achieve the advanced automatic operation of WtE plants, we will
strive to advance the ICT at waste disposal facilities with a view to expanding the application toother services such as industrial waste disposal treatment plants, sludge treatment/incineration
plants, and biomass power generation/sludge recycling facilities in addition to WtE plants, with ourMaiDAS® next-generation AI-based remote monitoring and operational support system built by thefusion of AI technologies—such as Machine Learning and especially Deep Learning—with various
network technologies including IoT.