Integrated Process Re-Design with Operation in the Digital Era: Illustration through an Industrial Case Study

Marcos, María P.; Pitarch, José Luis; Prada, César de

doi:10.3390/pr9071203

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…The second way is two-phase stochastic optimization when it is designed in uncertain conditions. The second phase variables correspond to the selected option from a set of possible scenarios, so they adapt the first phase to the selected scenario [32]. The level of failure of reengineering projects is up to 50% due to the low success rate of predicting the impacts and results of changes made by reengineering and the dynamic nature of the processes.…”

Section: Literature Reviewmentioning

confidence: 99%

Redesigning the Production Process Using Simulation for Sustainable Development of the Enterprise

et al. 2022

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In this case study, the medium-sized enterprise is looking for ways to achieve sustainable development. Following the Industry 4.0 trend could get the enterprise closer. The rate of information technology usage is very low in the enterprise. The enterprise’s problem is the long production of orders. The orders had to be rejected many times because the customer’s time limit could not be met. The major cause seems to be two environmentally insufficient and obsolete machines in the blast cleaning operation. These machines are replaced in the production by one new wheel blast machine. However, the production process must be redesigned and the layout changed to use this machine effectively. This redesign is difficult to verify and evaluate in the pilot plant experiment. Simulation is the most suitable tool to do it quickly and relatively easily. The ExtendSim simulation program creates a real production process model and then applies the redesign of this process in the model. The model must be thoroughly verified concerning the real production process to not distort the results. The result of the redesign of the production process is the reduction of the production duration by almost 50% and elimination of two environmentally insufficient and obsolete machines. Improved machine blast cleaning has also significantly reduced the utilization of blast cleaning. Spared capacities can be used for pre-production or otherwise for the profit-making of the enterprise. However, a new bottleneck appeared elsewhere in the production process by removing the previous one. The innovation passed through barriers, and Slovak small and medium enterprises started building sustainable development. The enterprise still must focus on further innovation, and more investment will be needed to achieve the final sustainable development goal.

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Section: Literature Reviewmentioning

confidence: 99%

Redesigning the Production Process Using Simulation for Sustainable Development of the Enterprise

et al. 2022

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“…This pressure drop is first created in the evaporation chambers by induced vacuum, and later by further condensation in an attached surface condenser, creating thus a multiple-effect evaporation. The efficiency of these type of plants (live-steam consumption per amount of evaporated water) is mainly determined by: 1) the performance of the cooling system and 2) the fouling state in the heat exchangers (due to deposition of organic residues present in the spinbath) [8]. Therefore, representative, but of limited complexity, models of these systems are needed to predict online the impact that the operation will have on the plant performance over time.…”

Section: Description Of the Case Study And Motivationmentioning

confidence: 99%

Machine Learning and the Digital Era from a Process Systems Engineering Perspective

Pitarch¹,

Prada²

2022

SNE

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Modern sensorization, communication and computational technologies provide collecting and storing huge amounts of raw data from large cyber-physical systems. These data should serve as the basis to take better decisions at all levels (from the design to operation and management). Nevertheless, raw data need to be transformed in useful information, usually in the form of prediction models. Machine learning plays a key role in this task. Process industry is not alien to this digital transformation, although large processing plants present particularities that differentiate them from other systems. These differences, if neglected, can make machine learning for general purpose fail in extracting the right information from data, leading thus to unreliable process models. As such models are the basis on which the ideas towards the cognitive plant rely, this issue is of major importance for a successful full digitalization of the process industry. In this paper the authors discuss these aspects, as well as some suitable machine-learning approaches, through their experience gained from applying advanced engineering in an industrial case study.

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“…Within this direction, the defining trend of the future material production of goods and services is the use of production networks with networkcentric control [1]. Examples of modern production sites are complexes of multifunctional computer numerical control (CNC) machines, 3D printers and robots, which are integrated into a network to create conditions for effective planning and optimal implementation of parallel technological processes [2][3][4][5]. A feature of technological processes is their adaptability to small-scale or piece production in such areas as mechanical engineering, processing of raw materials, assembly of multicomponent products, etc.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Simulation Modeling of Single and High‐Volume Machine‐Building Productions

Nikonova,

Savelyeva,

Zhetessova

et al. 2023

Preprint

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The authors of the article analyze the existing methods and models of technological preparation of machine-building industries. The structure of a three-level simulation model with network-centric control, the structures of individual elements of the simulation model and the process of simulation modeling are described. The criteria for choosing a rational option for the processing technological route have been determined. During the research, a simulation program was implemented in C++. It allows you to select the optimal scenario for the operation of a production site based on two criteria: time and cost. The volume of implementation is about 2x103 lines of code. A diagram of the modeling algorithm for the implemented program, a description of the classes and their interactions are given in the article. The developed simulation model was tested at a machine-building enterprise using the example of the “Pusher” part, manufactured under single-unit production condi-tions. The technological equipment used for the manufacture of this part was formed in the form of input data of the simulation model. The results of simulation modeling for the selected part are described. For each variant of the technological processing route, the values of variable costs and the duration of the production cycle were determined.

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Integrated Process Re-Design with Operation in the Digital Era: Illustration through an Industrial Case Study

Cited by 6 publications

References 19 publications

Redesigning the Production Process Using Simulation for Sustainable Development of the Enterprise

Redesigning the Production Process Using Simulation for Sustainable Development of the Enterprise

Machine Learning and the Digital Era from a Process Systems Engineering Perspective

Implementation of Simulation Modeling of Single and High‐Volume Machine‐Building Productions

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