Challenges of manufacturing for energy efficiency: towards a systematic approach through applications of machine learning

Nabati, Elaheh Gholamzadeh; Alvela Nieto, Maria Teresa; Bode, Dennis; Schindler, Thimo Florian; Decker, André; Thoben, Klaus-Dieter

doi:10.1590/0103-6513.20210147

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Research Article • Prod. 32 • 2022 • https://doi.org/10.1590/0103-6513.20210147 copy

Challenges of manufacturing for energy efficiency: towards a systematic approach through applications of machine learning

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Paper aims

Due to increasing energy prices, manufacturers have to pay more attention to the energy efficiency of their production processes. This paper aims to support manufacturers in increasing processes’ energy efficiency by using production data and applying machine learning approaches.

Originality

Systematic guidelines or standards for minimising the energy consumption of manufacturing processes through machine learning approaches are still lacking. This gap is addressed in this paper.

Research method

The paper follows a qualitative research method to understand the manufacturing processes and their challenges in improving energy efficiency. The raw data for a 5-step approach were collected in research projects with manufacturing SMEs, and information about the processes through interviews and workshops with them. Then, an analysis of currently available machine learning frameworks and their selection and implementation is conducted.

Main findings

The main result is a 5-step approach for increasing the energy efficiency of manufacturing processes through machine learning. Essential applications and technical challenges for data mapping, integrating, modelling, implementing, and deploying machine learning algorithms in manufacturing processes for increasing energy efficiency are presented.

Implications for theory and practice

The findings can guide manufacturers, researchers, and data scientists to use machine learning in practice when they intend to increase the energy efficiency of manufacturing processes.

Keywords:
Energy efficiency; Manufacturing processes; Machine learning

Aspect	Method-application	Technical challenge
Analysis of current processes regarding EE	Experts interview	Complex production setting, asking the right questions, working language, different viewpoints on the same problems
Identify higher consumers	Value-stream analysis	Processes or machines which consume the most energy
Illustration of material and energy flows in machinery, manufacturing equipment and plant	Process flow diagram (flowsheet)	Recognise all essential aspects, collection of energy consumption from different processes or machinery, which are not aggregated (combined) not available. Energy measurements should first be collected, and later combined together.
Need for a software framework that builds the connection between process mapping, data integration and modelling	Swagger	Collection of all required data sources, early recognition of data structure, need for having an overview and shortcut to remember complexities of the process during ML model development.

Aspect	Method-application	Technical challenge
Collect energy data from machinery and process	Sensors, flow meters, NIR devices	How many sensors, costs, sensor fusion problems
Automatic exchange of data from shop floor to database	Exchange technologies	Need for selection of right tool, standard interfaces, data fetching intervals and size
Store business and energy-related data	Databases, cloud storage	Incompatible data formats, need for controlling data quality, difficulty of recalling data to local computers
Select representative data views	Data tables	Need for domain knowledge, expert interview, energy-related problems, and their scope
Data quality monitoring	Outlier detection in the databases	Dirty data
Communication between devices	Machine-to-machine communication protocols, reading data from devices	Server shutdowns, difficulty of automatic data extraction

Aspect	ML algorithm	Technical challenge
Product or process features	Support Vector Machine (classification)	Parameter settings, labelled data
Product or process features	K-Means (clustering)	Data dimensionality, parameter settings, unlabelled data
Continuous or class-like outputs	Neural Networks (regression or classification)	Relevant features, overfitting, multiple outputs, labelled data
Continuous outputs	Support Vector Regression (Regression)	Relevant features, overfitting, labelled data

Aspect	Method-Task	Technical challenge
Optimise process variables	Evolutionary algorithms	Not visible (black-box), the range of variable combinations needs to be noted, unknown variable combinations
	Grid search	Same as evolutionary algorithms, additionally computation time growth with high-dimensional optimisation variable grids

No.		Suggestions
1	Lack of transparency of energy use	Identification of value-adding and non-value-adding processes, illustration of material and energy flows, identifying higher energy consumers (step 1 - “Process mapping”)
		Using data tools and interfaces for measuring energy, collect data in a structured form (step 2 - “Data integration”)
		Identifying variables of process and product that affect energy consumption through a data-driven modelling view (step 3 - “Modelling”)
		Visualisation of energy-saving opportunities for key energy drivers and process settings (step 5 - “Process control”)
2	Need for structured product and process-related data	Data collection through sensors, flow meters, camera devices, using data technologies such as database, swagger for data storage and linkage between them (step 2 - “Data integration”)
		Automatic data transfer and exchange through protocols, data quality monitoring (step 2 - “Data integration”)
		Structuring employee’s knowledge about the product quality process configurations through interviews, Swagger (step 1 - “Process mapping”)
3	Lack of reliability of employees to the robustness of sensor values or automated measurement systems	System for monitoring the accuracy of measurements (step 5 - “Process control”)
4	Adapt to future digitalisation and automating manufacturing process	Assistant systems (step 5 - “Process control”)
		Continuous learning (update) of deployed models (step 3 - “Modelling”, step 4 - “Optimisation”, step 5 - “Process control”)

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E-mail: production@editoracubo.com.br

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