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

One Stop Solution for Everything from Prediction toFailure and Influencing Factor Detection
AI Support for Biomass Power Plant Business

YUSUKE YAMASHINA*1

YUDAI KATO*2

TAKANOBU MATSUBARA*3

Among renewable energy sources that are spreading toward the realization of a low carbon
society, biomass power generation is expected to contribute to waste reduction. Meanwhile,against the background where operation & maintenance (O&M) has been advanced due to theprogress of AI&IoT technology, the optimization of entire production lines across equipment orentire plants, in addition to the stable operation of individual pieces of equipment, is desired.
This report presents a case study of utilizing AI&IoT technology for the O&M of a biomass
power plant and realizing the improvement of efficiency in operation management.

|1. Introduction
The use of renewable energy has been spreading toward the creation of a recycling-oriented
society aiming at reducing waste in response to growing interest in greenhouse gas control on aglobal scale. In recent years, making a contribution to regional revitalization is becoming moreimportant, so biomass power generation that can use food waste and livestock manure as an energy
source is attracting attention.
However, there are many biomass power plants that are not operated and controlled by a
distributed control system (DCS), etc., because they are smaller in scale than thermal power plants
and chemical plants. In particular, biomass power generation has the problem that it is difficult tomake a power generation plan because the amount of generated biogas accompanying methane
fermentation largely depends on the properties of collected resources to be used and the activity
state of microorganisms and the monitoring and control of these with sensors is difficult. Inaddition, management of amount of biogas generated is based on implicit matters such as theknowledge and experience of workers, so the succession of skills is also a problem.
This paper introduces how the O&M of a biomass power plant changes by applying theENERGY CLOUDTM Service, which is a comprehensive energy solution service utilizing
proprietary AI&IoT technology, based on the case example of Biomass Power Shizukuishi Co.,Ltd.

|2. BPS
2.1

Business scheme

Biomass Power Shizukuishi Co., Ltd. (BPS) is the first private-sector-led biomass power
generation company in Japan established through investments by Koiwai Farm, Ltd., Shizukuishi
Town, Tohoku Electric Power Engineering & Construction Co., Inc., Tokyo Sangyo Co., Ltd., andMitsubishi Heavy Industries Environmental & Chemical Engineering Co., Ltd. BPS conducts
regional recycling of resources and energy through the complex processing of livestock waste andfood waste from Koiwai Farm.
Figure 1 shows the processing flow of BPS. Livestock manure collected from Koiwai Farmis solid-liquid separated into solid matter and residue-removed liquid. The residue-removed liquid*1CIS Department, ICT Solution Headquarters*2

Power & Energy Solution Business Planning Department, Power & Energy Solution Business Division, Power Systems*3

Chief Staff Manager, ICT Project Department, ICT Solution Headquarters

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
2is put into a methane fermentation tank together with food residue collected from the surroundingarea. In the methane fermentation tank, biogas including methane gas and digestive liquid (liquidafter methane gas recovery), which is a fermentation residue, are generated. The methane gas isused for power generation, the generated electric power is used on-site, and the surplus electricity issold. The digestive liquid, which is a fermentation residue, is returned to agricultural land as liquid
fertilizer, and the solid content of livestock manure resulting from solid-liquid separation is sold asgood compost.

Figure 1

2.2

Processing flow of Biomass Power Shizukuishi

Problems of O&M in BPS

Biomass power generation business that supplies regional-recycling carbon-free energy
requires improvement of business profitability through efficient operation. Figure 2 illustrates theproblems in BPS.

Figure 2

Problems in BPS(1) A wide variety of resources are delivered to BPS. In addition, BPS accepts such resources
every day, because farms have no days off. Therefore, it is extremely difficult to accurately
ascertain when and how much biogas will be generated. Biogas is temporarily stored in thegas holder, but the surplus is burned when the generated biogas exceeds the storage capacity

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
3of the gas holder. Conventionally, the timing and amount fed into the fermentation tank isadjusted so that no surplus occurs based on empirical judgment.
(2) Since the facilities are not fully equipped with various sensors, the state of equipment isconfirmed by patrol checks, so abnormalities are dealt with when they occur. Delays inresponse to abnormalities are directly linked to business operational risks, but there is nomechanism to easily detect abnormalities at present.
(3) Since the properties of produced biogas vary depending on the resources being used, theamount of electricity generated varies even when the amount of biogas is constant, making it
difficult to plan for selling the power.
ENERGY CLOUDTM is used to solve these problems, and Figure 3 presents an outline of
how they were solved. For the details of ENERGY CLOUDTM, refer to the introduction in thisfeature issue.

Figure 3 Outline of how problems are solved|3. Business operation utilizing AI
3.1

Prediction of amount of biogas generated

To efficiently generate electricity while accepting a wide variety of resources, the amount ofbiogas generated is predicted using AI technology and the possibility of making operational
decisions in advance is verified.
To predict the amount of biogas generated, it is necessary to construct prediction expressions
in a deductive manner based on the components of the delivered materials, the decomposition rate,etc. However, it is not easy to uniformly define these factors for a wide variety of resources. Inaddition, because such a wide variety of resources is accepted, there is the possibility that theresources may contain toxic substances that inhibit the activity of organisms. Furthermore, as canbe seen in the process flow in Figure 1, there is a residence time of several days at each facility, anda time lag occurs between the feeding of resources and the generation of biogas, so previously it
was difficult to accurately predict the amount of biogas to be generated.
With ENERGY CLOUDTM, it is possible to perform analysis utilizing not only theoperational data and various sensor data acquired by DCS, but also information such as daily
operational reports and production plans. In the case of this verification as well, whether theinductive prediction of the 1-day later, 2-day later, and 3-day later gas generation amounts ispossible is examined. For analysis with ENERGY CLOUDTM, a prediction technique adopting anensemble learning method incorporating proprietary technology is used. Table 1 gives theverification conditions and results, and Figure 4 presents the prediction results on August 26, 2016.
The error is only 13% even for the amount of biogas generated after 3 days, for which thedivergence is largest, meaning that accurate prediction is possible. As a result, it was confirmedthat applying ENERGY CLOUDTM makes it possible to make operational decisions in advance. In
this verification, the prediction error is defined as the value obtained by averaging over theverification period the values obtained by calculating the ratio of the difference between the true
value and the predicted value on a daily basis.

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

Table 1 Verification conditions and results
Item

1-day later prediction

Learning data

3-day later prediction

Nov. 1, 2006 - Jun. 30, 2016

Verification data

Jul. 1, 2016 - Sep. 30, 2016

Prediction condition

Prediction of 1-day later Prediction of 2-day later Prediction of 3-day latergas generation amountgas generation amountgas generation amount

Explanatory variable

Daily report (delivered amount)

Prediction error

Figure 4

3.2

2-day later prediction

10%

12%

13%

Prediction results (on August 26 for example)

Condition management of machinery and equipment

This section presents condition management using IoT technology. The receiving facility ofBPS uses a crushing pump with cutting blades to make utilized resources finer. However, sincevarious kinds of resources are delivered, sometimes problems caused by blockage occur. Theseproblems, which are difficult to detect using methods other than daily patrol checks, are dealt withwhen they occur. However, the delay in response is one of the factors that increase the business
operational risk. Therefore, the condition management of machinery and equipment utilizing
Netmation eFinderTM, which is introduced in another report in this feature, is verified.

Figure 5

Abnormality detection results

For condition management, an anomaly detection technique using the crushing pump
operational data acquired by Netmation eFinderTM is used as an explanatory variable. The anomaly
detection technique is a method of defining a normal space for time series data and calculating thedegree of abnormality by obtaining the distance from the space. Figure 5 shows the calculation
results of the degree of abnormality. As a result of interviews with project participants, the timing
at which the degree of abnormality sharply increased coincides with the occurrence of blockage, soMitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)5TM

it was confirmed that abnormalities could be detected. With ENERGY CLOUD , the analysis
results can be checked on a browser, which makes it possible to detect blockages from an officeand contributes to the lowering of the business operational risk due to the quick response time andalso to a reduction of the frequency of patrol checks (labor saving). The short-term increase in thedegree of abnormality given in Figure 5 indicates a problem associated with blockage. On the otherhand, the medium or long-term increase in the degree of abnormality, which decreases after themaintenance of the equipment, can be regarded as age-related deterioration of the equipment. As aresult, it was confirmed that not only can detection of sudden abnormalities be achieved, but so canthe optimization of part replacement timing.

3.3

Biogas properties

Finally, by using ENERGY CLOUDTM, the properties of the biogas to be generated (theamount of power generation per unit amount) can be visualized and conditions that will affects thebiogas properties if they occur simultaneously are automatically extracted to clarify the conditions
under which the amount of power generation increases or decreases. Figure 6 and Figure 7
provide examples of the visualization of the biogas properties and the results of condition
extraction, respectively. Figure 6 indicates that the amount of power generation fluctuates evenwith a constant amount of biogas generated. The condition extraction illustrated in Figure 7 uses
the high power generation zone and the low power generation zone that are classified by thevisualization of the biogas properties as objective variables to extract the conditions classified into
each zone using a decision tree. This example indicates that when the gas generation amount on theprevious day is large, high power generation tends to occur. In this way, planning of power selling
can be supported by grasping the biogas properties in advance. In addition, it is possible toformalize the knowledge cultivated by the experience of skilled workers and support the sharingand transfer of technology.

Figure 6 Visualization of biogas
properties

Figure 7

Results of condition extraction|4. Conclusion
This paper presented the O&M of a biomass power plant applying the ENERGY CLOUDTM
Service, which is a comprehensive energy solution service utilizing proprietary AI&IoTtechnology.
The ENERGY CLOUDTM Service made possible the prediction of the amount of biogas
generation, which was conventionally difficult, with high accuracy based only on daily report dataand changes of the preceding operation conditions, and also facilitated stable and efficient
operation. In addition, by utilizing the IoT device Netmation eFinderTM, it is possible to manage thecondition of machinery and equipment, to detect abnormalities early on and to optimize andvisualize the maintenance timing. Furthermore, by extracting the conditions that affect the biogas
properties, a contribution to technology transfer can be made.
Going forward, we will consider the utilization of AI from a management perspective, suchas the pricing of utilized resources according to the amount of generated biogas, and develop
technologies that can support O&M from a wider perspective.

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
6TMENERGY CLOUD and all related marks and logos are registered trademarks of Mitsubishi Heavy Industries,
Ltd. in Japan and other countries.
Netmation eFinderTM and all related marks and logos are registered trademarks of Mitsubishi Hitachi Power
Systems, Ltd. in Japan.

References(1)(2)

S.Harada et.al, Combined Biomass Power Generation Project at Koiwai Farm in Shizukuishi，Mitsubishi
Juko giho，Vol.42 No.4 (2005)
Biomass Power Shizukuishi Co., Ltd.，http://www.bps-koiwai.co.jp