Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
1

One Stop Solution from Understanding
Facts, Setting Goals, to Optimization
AI Support for Plant Operation

YUDAI KATO*1YUSUKE YAMASHINA*2

HIDEHIRO SHINOHARA*3 MIZUHO HIRATA*4YOSHINORI GOTO*5NORITAKA KANAI*6

This report describes a project case example in which the ENERGY CLOUDTM Service,
MHI's comprehensive energy solution service using its own AI&IoT technologies, was applied forthe improvement of operational efficiency at a Waste to Energy plant. As an approach to solving
problems, the optimization was performed through an understanding of the current state from theaspects of both the operator and plant and based on the setting of goals with experts from various
fields being involved. As a result, the calorific value of the waste to be incinerated in the future waspredicted with an accuracy of 95% or greater, and the operational guidance into which theexpertise of experienced operators was incorporated was presented, thereby realizing support for
operational decision-making that is quicker and independent of individual skill.

|1. Introduction
In recent years, the environment surrounding plant O&M (Operations and Maintenance) has
been changing as listed below and solutions that differ from those of existing approaches are indemand.
(1) Increase in market needs for more inexpensive and higher quality services and theprolongation of plant service life(2) Manifestation of the problem in transferring operator skills due to work force reduction andan increase in operational complexity(3) High-speed and inexpensive implementation of information processing and communications
realized by the progress of ICT technology

Figure 1*1

Overview of Jukan Operation Co., Ltd. (JKO)

Power & Energy Solution Business Planning Department, Power & Energy Solution Business Division, Power Systems*2CIS Department, ICT Solution Headquarters*3General Manager, Energy & Environment Solution Division, Yokohama Energy & Environment Solution Department, MHPS

*4Energy & Environment Solution Division Yokohama Energy Solution Department, MHPS Control Systems Co., Ltd.

*5Office Manager, Group Technology Promotion Office, JUKAN OPERATION CO., LTD.

*6

Professional Manager, Group Technology Promotion Office, JUKAN OPERATION CO., LTD.

Control Systems Co., Ltd.

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
2JUKAN OPERATION CO., LTD. (JKO), which is an MHI Group company dealing withplant O&M, possesses safe and stable operation and preventive maintenance technologies andpromotes operations that increase the values of facilities. An overview of JKO is shown in Figure 1.
To address the changing environment surrounding plant O&M and provide higher-quality
services to customers, JKO has been seeking solutions that improve operational efficiency. Under
the circumstances, MHI applied the ENERGY CLOUDTM Service, which is a comprehensive
energy solution service using its own AI&IoT technologies, to the Waste to Energy plant that JKOoperates and manages and realized the improvement of operational efficiency in the field.
This report introduces the content of the project implemented toward solving a series ofproblems and the results are described as a case example.

|2. Content of the project implemented toward solving problems
In a problem solving project that is a way of obtaining insight from data using AI (ArtificialIntelligence) and IoT (Internet of Things), it is important to properly understand the current state
and then sharply narrow down goals. Being devoted to only collecting data with abstract goals
being set or the introduction of a system as a makeshift solution will cause economic and temporallosses, resulting in the deterioration of customer value.
MHI/JKO, which have ENERGY CLOUDTM, our own AI&IoT technologies, and MHPS
Control Systems Co., Ltd. (MHPS-CS), which features strength in system solutions, tried to solve
problems through the following approach:(1) Understanding of current state: Understanding of the current onsite state from the aspect ofboth the operator and plant and narrowing down of targets(2) Setting of goals: Co-creation and consensus formation through discussions among persons
concerned from various perspectives(3) Optimization: Prediction of future using ENERGY CLOUDTM and provision of guidance
based on the prediction
An outline of the content of the approach is given in Figure 2 and the details will be
described later.

Figure 2

2.1

Outline of the approach

Understanding of current state

For the purpose of objectively understanding the actual state and then narrowing down thesystem implementation targets, first a questionnaire survey was conducted to multiple plants which
conduct Waste to Energy plant operations. Operational loads visualized by kind of operation andtime zone based on the survey results are presented in Figure 3. In some tasks, the operational load
is high only in a certain time zone. In other tasks, operations are conducted continuously for 24hours. Thus, it shows that the characteristics differ by the content of the operation. The
visualization of processes like this eliminates biases such as "misunderstanding by the person incharge" and allows the narrowing-down of system implementation targets that have a higher
investment recovery effect.

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)3

Figure 3

Example of visualized operational loads of operators

It is also important to understand plant operating state and characteristics, as well as humanoperational loads. Several hundred pieces of time-series operational data per system were extracted
from DIASYS NetmationTM, which is a Distributed Control System (DCS) that has already been
introduced to the plant, and were subjected to the Exploratory Data Analysis (EDA) forvisualization, the exploration of patterns and extraction of correlations. DIASYS NetmationTM has
been supplied to more than 2000 projects in the world and has been well received because of its
high reliability and precise functions. The appearance of DIASYS NetmationTM and the delivery
results are illustrated in Figure 4.
These steady efforts to understand the current state are very important in defining a baseline
toward the success of the project.

Figure 4

2.2

Appearance of DIASYS NetmationTM and delivery results (1)Setting of goals

Based on the results of the understanding of the current state, discussions were made among
the persons concerned from various perspectives. As a result, the assumption was set that theprediction of the "waste calorific value," which is an entry condition from outside the plant, could
improve operational efficiency inside the plant.
In this case example, the Key Factors for Success (KFS) in the setting of goals are as follows(Figure 5):(1) Setting of appropriate scope
For the improvement of operational efficiency from a narrow perspective, problems aresolved in a single equipment or per unit, and from a broad perspective, problems are solved inan economic unit (ecosystem) including the plant and its upstream and downstream. From
which perspective problems should be solved depends on the user's intention and the scope ofdata. For example, in the case of problem solving in a single equipment, the equipment andperipheral data are needed. On the other hand, in the case of problem solving in an ecosystem,
sufficient data from outside the plant are needed, but the importance of the characteristic data ofsingle equipment is relatively lowered.

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
4(2)Involving experts from various fields
In this case example, experts from various fields, such as operating tasks, plant design,
data science, system implementation and business, made discussions and set the goals. It is
important to incorporate complex perspectives such as onsite feasibility and acceptability.
(3) Agile project implementation as a precondition
Concerning the method of solving problems by using AI (Artificial Intelligence) andobtaining insight from data, there is a high possibility that it will not be economically viable atthe present technological level if accuracy close to 100% is required. Rather than adhering to anincrease in accuracy, which results in a delay in decision-making, agile project implementation
is more important in which a project is conducted quickly on a small scale with an appropriate
level of accuracy to accelerate the evaluation cycle.

Figure 5

2.3

Key Factors for Success (KFS) in the setting of goals

Optimization

A system in which ENERGY CLOUDTM is used to predict the calorific value of the waste tobe incinerated one hour later based on the set goals was established. The prediction results aredepicted in Figure 6. In the evaluation period of one month, a prediction accuracy of 95% orgreater was achieved. It was confirmed that the system can sufficiently support the decision makingof operators. The accuracy is defined with the predictive relative error (rate of predictive error
relative to true value) being as the difference from 100%.

Figure 6

Prediction results for the waste calorific value by ENERGY CLOUDTM

In addition, the function of performing operational guidance based on the waste calorificvalue predicted using ENERGY CLOUDTM and the expertise of experienced operators wasconstructed using DIASYS NetmationTM. An example of the operational guidance screen can beseen in Figure 7.
In this function, the operational indices to operational parameters are computed based on thepredicted value for the calorific value of the waste to be incinerated in the future, and if any current
value deviates from a certain range, it is indicated in red, thereby supporting the decision-making
on operation which is quicker and more independent of individual skill compared to theconventional system, allowing the realization of the improvement of operational efficiency. By

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
5realizing the improvement of operational efficiency, safe operation and the prolongation of theservice life of the plant can also be promoted.

Figure 7

Example of the operational guidance screen|3. Conclusion
This report described the solution by which ENERGY CLOUDTM is used to predict the waste
calorific value with an accuracy of 95% or greater and to provide operational guidance for thepurpose of improving operational efficiency at a Waste to Energy plant. In the future, we willcontribute to the formation of a recycling-oriented society through the expansion of customer
contents and the improvement of technologies.
This technology and the approach to the solution can be applied not only to Waste to Energy
plants, but also to various fields such as power generation, chemical plants and manufacturing
equipment. We will make efforts to solve various issues mainly focusing on the energy andenvironmental fields.
ENERGY CLOUDTM and all related marks and logos are registered trademarks of Mitsubishi Heavy Industries,
Ltd. in Japan and other countries.
DIASYS NetmationTM and all related marks and logos are registered trademarks of Mitsubishi Hitachi PowerSystems, Ltd. in Japan and other countries.

References(1)

Control Systems What is DIASYS?

https://www.mhps.com/products/control-systems/outline/