Achieving Circular and Efficient Production Systems: Emerging Challenges from Industrial Cases

The article presents the main factors of the industrial system of the enterprise and their development in evolution of scientific organization of production. The main principles underlying the efficient production system have been described. The comparison of the main stages of the development of the science of production organization and management has been done.

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Systematic combination of Lean Management with digitalization to improve production systems on the example of Jidoka 4.0

International Journal of Engineering Business Management ◽

10.1177/1847979020951351 ◽

2020 ◽

Vol 12 ◽

pp. 184797902095135 ◽

Cited By ~ 1

Author(s):

Jochen Deuse ◽

Uwe Dombrowski ◽

Fabian Nöhring ◽

Jürgen Mazarov ◽

Yannick Dix

Keyword(s):

Information And Communication Technologies ◽

Production Systems ◽

Communication Technologies ◽

Efficient Production ◽

Lean Management ◽

Research Project ◽

Existing Structures ◽

Industrial Companies ◽

Information And Communication ◽

Cyber Physical Production Systems

Lean Management builds the basis for efficient production systems for many industrial companies. However, lots of potentials of Lean Management have been lifted and information and communication technologies in the context of digitalization and cyber-physical production systems (CPPS) offer new possibilities to enhance the performance of companies. Even though surveys indicate that companies recognize these potentials, especially small and medium-sized companies still face challenges in selection and implementation of suitable solutions. Thus, the research project GaProSys 4.0 aims at supporting companies with a systematic approach to combine existing structures of Lean Management with potentials of digitalization in development of a new set of methods to enhance production systems. This paper presents the approach of the research project to develop a structured set of methods and provides an example to illustrate the potentials.

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The Methodology of Virtual Foundry Developing

Archives of Metallurgy and Materials ◽

10.2478/amm-2013-0089 ◽

2013 ◽

Vol 58 (3) ◽

pp. 867-870 ◽

Cited By ~ 1

Author(s):

M. Brzeziński ◽

A. Stawowy ◽

R. Wrona

Keyword(s):

Simulation Model ◽

Production Planning ◽

Production Systems ◽

State Of The Art ◽

The State ◽

Systemic Approach ◽

Integrative Approach ◽

Efficient Production ◽

Economic Aspects

Abstract Systemic approach to design of factories requires that engineering, organisational and economic aspects should be considered concurrently. That prompts the need to develop a solution, based on the state-of-the-art IT technologies, to enable us to solve the problems associated with foundry production planning. The paper outlines a methodology of creating the simulation model of a virtual foundry, as a tool for foundry design. An integrative approach is suggested for development of a complete foundry model, enabling the design of more efficient production systems. The underlying principles of such models are discussed, the basic stages involved in the methodology are outlined and the range of its applicability is defined.

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Production of alkanes from CO2 by engineered bacteria

10.1101/346536 ◽

2018 ◽

Author(s):

Tapio Lehtinen ◽

Henri Virtanen ◽

Suvi Santala ◽

Ville Santala

Keyword(s):

Production Systems ◽

Liquid Fuels ◽

Modular System ◽

Fatty Aldehyde ◽

Efficient Production ◽

Acinetobacter Baylyi ◽

Single Organism ◽

Fuels And Chemicals ◽

Microbial Biosynthesis ◽

Long Chain

AbstractBackgroundMicrobial biosynthesis of alkanes is considered a promising method for the sustainable production of drop-in fuels and chemicals. Carbon dioxide would be an ideal carbon source for these production systems, but efficient production of long carbon chains from CO2is difficult to achieve in a single organism. A potential solution is to employ acetogenic bacteria for the reduction of CO2to acetate, and engineer a second organism to convert the acetate into long-chain hydrocarbons.ResultsIn this study, we demonstrate alkane production from CO2by a system combining the acetogenAcetobacterium woodiiand a non-native alkane producerAcinetobacter baylyiADP1 engineered for alkane production. Nine synthetic two-step alkane biosynthesis pathways consisting of different aldehyde- and alkane-producing enzymes were combinatorically constructed and expressed inA. baylyi.The aldehyde-producing enzymes studied were AAR fromSynechococcus elongatus,Acr1 fromA. baylyi,and Ramo, a putative dehydrogenase, fromNevskia ramosa.The alkane-producing enzymes were ADOs fromS. elongatusandNostoc punctiforme,and CER1 fromArabidopsis thaliana.The performance of the pathways was evaluated with a twin-layer biosensor, which allowed the monitoring of both the intermediate, fatty aldehyde, as well as the alkane production. The highest alkane production, as indicated by the biosensor, was achieved with a pathway consisting of AAR and ADO fromS. elongatus.The performance of this pathway was further improved by balancing the relative expression levels of the enzymes in order to limit the accumulation of the intermediate fatty aldehyde. Finally, the acetogenA. woodiiwas used to produce acetate from CO2and H2, and the acetate was used for alkane production by the engineeredA. baylyi,thereby leading to the net production of long-chain alkanes from CO2.ConclusionsA modular system for the production of drop-in liquid fuels from CO2was demonstrated. Among the studied synthetic pathways, the combination of ADO and AAR fromS. elongatuswas found to be the most efficient in heterologous alkane production inA. baylyi.Furthermore, limiting the accumulation of the fatty aldehyde intermediate was found to be beneficial for the alkane production.

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The algae revolution 2.0: the potential of algae for the production of food, feed, fuel and bioproducts – why we need it now

The Biochemist ◽

10.1042/bio_2021_190 ◽

2021 ◽

Author(s):

Stephen Mayfield ◽

Michael Burkart

Keyword(s):

Carbon Dioxide ◽

Natural Gas ◽

Production Systems ◽

Efficient Production ◽

Energy Reserves ◽

Animal Life ◽

Algae Biomass ◽

The World ◽

New Thinking ◽

Underground Reservoirs

Algae made our world possible, and it can help us make the future more sustainable; but we need to change the way we live and adopt new more efficient production systems, and we need to do that now. When the world was new, the atmosphere was mainly carbon dioxide, and no animal life was possible. Along came algae with the process of photosynthesis, and things began to change. Ancient cyanobacteria algae turned carbon dioxide into enormous sums of lipids, proteins and carbohydrates, while they secreted oxygen into the atmosphere. Over a billion years, as oxygen filled the air and algae filled the seas, animal life became possible. Eventually all that algae biomass became petroleum and natural gas, which for eons sat undisturbed in vast underground reservoirs, holding enormous sums of untapped energy. Less than 200 years ago humans learned to tap these energy reserves to create the world we know today, but in so doing, we have released millions of years of stored CO2 back into the atmosphere. Algae can again help make the world a better place, but this will require new thinking and new ways of producing our food, feed and fuels. We need an algae revolution 2.0.

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From remote sensing to bioeconomy: how big data can improve automatic map generation

10.5194/egusphere-egu2020-20655 ◽

2020 ◽

Author(s):

Jonathan Rizzi ◽

Ingvild Nystuen ◽

Misganu Debella-Gilo ◽

Nils Egil Søvde

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Big Data ◽

Climate Adaptation ◽

Technology Development ◽

Production Systems ◽

Learning Algorithm ◽

Efficient Production ◽

Research Activity ◽

Social Mission

Recent years are experiencing an exponential increase of remote sensing datasets coming from different sources (satellites, airplanes, UAVs) at different resolutions (up to few cm) based on different sensors (single bands sensors, hyperspectral cameras, LIDAR, &#8230;). At the same time, IT developments are allowing for the storage of very large datasets (up to Petabytes) and their efficient processing (through HPC, distributed computing, use of GPUs). This allowed for the development and diffusion of many libraries and packages implementing machine learning algorithm in a very efficient way. It has become therefor possible to use machine learning (including deep learning methods such as convolutional neural networks) to spatial datasets with the aim of increase the level of automaticity of the creation of new maps or the update of existing maps.&#160;Within this context, the Norwegian Institute of Bioeconomy Research (NIBIO), has started a project to test and apply big data methods and tools to support research activity transversally across its divisions. &#160;NIBIO is a research-based knowledge institution that utilizes its expertise and professional breadth for the development of the bioeconomy in Norway. Its social mission entails a national responsibility in the bioeconomy sector, focusing on several societal challenges including: i) Climate (emission reductions, carbon uptake and climate adaptation); ii) Sustainability (environment, resource management and production within nature and society's tolerance limits); iii) Transformation (circular economy, resource efficient production systems, innovation and technology development); iv) food; and v) economy.The presentation will show obtained results focus on land cover mapping using different methods and different dataset, include satellite images and airborne hyperspectral images. Further, the presentation will focus related on the criticalities related to automatic mapping from remote sensing dataset and importance of the availability of large training datasets.

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Ensemble Neural Network Model for Predicting the Energy Consumption of a Milling Machine

Volume 4: 20th Design for Manufacturing and the Life Cycle Conference; 9th International Conference on Micro- and Nanosystems ◽

10.1115/detc2015-47957 ◽

2015 ◽

Cited By ~ 2

Author(s):

Ronay Ak ◽

Moneer M. Helu ◽

Sudarsan Rachuri

Keyword(s):

Energy Consumption ◽

Prediction Model ◽

Prediction Models ◽

Production Systems ◽

Face Milling ◽

Model Parameters ◽

Efficient Production ◽

Milling Machine ◽

Energy Prediction

Accurate prediction of the energy consumption is critical for energy-efficient production systems. However, the majority of existing prediction models aim at providing only point predictions and can be affected by uncertainties in the model parameters and input data. In this paper, a prediction model that generates prediction intervals (PIs) for estimating energy consumption of a milling machine is proposed. PIs are used to provide information on the confidence in the prediction by accounting for the uncertainty in both the model parameters and the noise in the input variables. An ensemble model of neural networks (NNs) is used to estimate PIs. A k-nearest-neighbors (k-nn) approach is applied to identify similar patterns between training and testing sets to increase the accuracy of the results by using local information from the closest patterns of the training sets. Finally, a case study that uses a dataset obtained by machining 18 parts through face-milling, contouring, slotting and pocketing, spiraling, and drilling operations is presented. Of these six operations, the case study focuses on face milling to demonstrate the effectiveness of the proposed energy prediction model.

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Principles and Design Strategies for Ultra‐efficient Production Systems in the Process Industry

Chemie Ingenieur Technik ◽

10.1002/cite.202100062 ◽

2021 ◽

Author(s):

Maximilian Schutzbach ◽

Johannes Full ◽

Steffen Kiemel ◽

Lara Waltersmann ◽

Lennard Sielaff ◽

...

Keyword(s):

Production Systems ◽

Process Industry ◽

Efficient Production ◽

Design Strategies

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Principles of Modular Architecture in the Manufacturing Technology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.309.105 ◽

2013 ◽

Vol 309 ◽

pp. 105-112 ◽

Cited By ~ 6

Author(s):

Jozef Svetlík ◽

Peter Demeč

Keyword(s):

Manufacturing Systems ◽

Production Systems ◽

Manufacturing Technology ◽

Point Of View ◽

Efficient Production ◽

Flexible Production ◽

Modular Architecture

The article deals with the issue of modularity as a tool for flexible and efficient production systems. When having closer look at implemented terms in production structures there emerges amount of terms used, such as: flexible, modular, modular manufacturing systems, which are often viewed from a subjective point of view. Due to efforts to standardize and clarify the terms referred above this paper brings definitions of flexible production structures terminology, their relationships and new idea for the efficient division.

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