The article presents an idea of a production process control system. Advanced automation and control of production processes play a key role in maintaining competitiveness. The proposed solution consists of sensor networks for measurement process parameters, production resources and equipment state. The system uses wired and wireless communication, which gives possibility to acquisition data from existing in enterprise sensors and systems as well as acquisition data from new systems and sensors used to measure all processes, starting from production preparation to the final product. The solution contains process tomography sensors based on electrical capacitance tomography, electrical impedance tomography and ultrasound tomography. The use of tomographic methods enables to manage the intelligent structure of the companies in terms of processes and products. Industrial tomography enables observation of physical and chemical phenomena without the need to penetrate inside. It will enable the optimization and auto-optimization of design processes and production. Such solutions can operate autonomously, monitor and control measurements. All sensors return to the system continuous data about state of processes in some technologically closed objects like fermenters. Process tomography can also be used to acquisition data about a flow of liquids and loose ingredients in pipeline based on transport systems. Data acquired from sensors are collected in data warehouses in order to future processing and building the knowledge base. The results of the data analysis are showed in user control panels and are used directly in the control of the production process to increase the efficiency and quality of the products. Control methods cover issues related to the processing of data obtained from various sensors located at nodes. Monitoring takes place within the scope of acquired and processed data and parameter automation.


process tomography; manufacturing execution system; production control system

Baker A.D.: A Survey of Factory Control Algorithms which Can be Implemented in a Multi-Agent Heterarchy: Dispatching, Scheduling, and Pull. Journal of Manufacturing Systems 1998.

Barkmeyer E., Denno P., Feng S., Jones A., Wallace E.: NIST Response to MES Request for Information. National Institute of Standard, Gaithersburg 1999.

Christensson P.: Web Service Definition. [23.02.2018].

Consulting, Burleson. Oracle history. [14.02.2018].

ECMA. The JSON Data Interchange Syntax. s.l. :, 2017.

Elragal A., Haddara M.: The Future of ERP Systems: look backward before moving. Procedia Technology 5/2012.

Fletcher M., Garcia-Herreros E., Christensen J.H., Deen S.M., Mittmann R.: An Open Architecture for Holonic Cooperation and Autonomy.

Giret A., Botti V.: Holons and Agents. Journal of intelligent manufacturing 15/2004.

International Business Machines Corporation (IBM), Eurotech. MQTT v3.1 Protocol Specification. s.l. :, 2014.

Jacobs F.R., Weston Jr Ted F.C.: Enterprise resource planning (ERP)–A brief history. Journal of Operations Managem.. 25/2007.

Kletti J., Deisenroth R.: MES Compendium: Perfect MES Solutions based on HYDRA. Springer, 2012.

Kłosowski G., Gola A.: Risk-based estimation of manufacturing order costs with artificial intelligence. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, IEEE, 2016, 729–732.

Leitão P., Colombo A.W.: Petri net based Methodology for the Development of Collaborative Production Systems l. IEEE, 2006.

Leitao P.: Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence 22/2009.

Mazurkiewicz D.: Maintenance of belt conveyors using an expert system based on fuzzy logic. Archives of Civil and Mechanical Engineering 15(2)/2015, 412–418.

MESA. Enterprise-Control System Integration Part 1: Models and Terminology. MESA, 2000.

Meyer H., Fuchs F., Thiel K.: Manufacturing Execution Systems. Optimal Design, Planning, and Deployment. McGraw-Hill, 2009.

NAMUR. Functions and Examples of Operations Control Level Solutions. Technical report. Normenarbeitsgemeinschaft für Meß- und Regeltechnik in. 2003.

Polakowski K., Filipowicz S., Sikora J., Rymarczyk T.: Tomography Technology Application for Workflows of Gases Monitoring in The Automotive Systems. Przegląd Elektrotechniczny 84(12)/2008, 227–229.

Polakowski K., Filipowicz S.F., Sikora J., Rymarczyk T.: Quality of imaging in multipath tomography. Przeglad Elektrotechniczny 85(12)/2009, 134–136.

Rouse M.: Microservices. http://searchmicroservices. [23.02.2018].

Rymarczyk T., Filipowicz S., Sikora J., Polakowski K.: A piecewise-constant minimal partition problem in the image reconstruction. Przegląd Elektrotechniczny 85(12)/2009, 141–143.

Rymarczyk T., Sikora J., Waleska B.: Coupled Boundary Element Method and Level Set Function for Solving Inverse Problem in EIT. 7th World Congress on Industrial Process Tomography, WCIPT7 2013, 312–319.

SAP [14.02.2018].

Schmidt A., Otto B., Österle H.: A Functional Reference Model for Manufacturing Execution Systems in the Automotive Industry. Wirtschaftinformatik Proceedings 89/2011.

Smolik W., Radomski D.: Performance evaluation of the iterative image reconstruction algorithm with sensitivity matrix updating based on real measurements for electrical capacitance tomography. Measurement Science and Technology 20(11)/2009, 115502.

Trentesaux D.: Distributed control of production systems. Engineering Applications of Artificial Intelligence 22/2009.

Vitliemov P.: About Manufacturing Execution Systems. Proceedings of the University of Ruse 52(5.1)/2013.

W3C. Extensible Markup Language (XML) 1.0 (Fifth Edition). s.l.: W3C, 2008.

Wajman R., Fiderek P., Fidos H., Jaworski T., Nowakowski J., Sankowski D., Banasiak R.: Metrological evaluation of a 3D electrical capacitance tomography measurement system for two-phase flow fraction determination, Meas. Sci. Technol. 24/2013, 065302.


Published : 2018-09-25

Niderla, K., Rymarczyk, T., & Sikora, J. (2018). MANUFACTURING PLANNING AND CONTROL SYSTEM USING TOMOGRAPHIC SENSORS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 8(3), 29-34.

Konrad Niderla
Research and Development Center, Netrix S.A., Lublin  Poland
Tomasz Rymarczyk 
1Research and Development Center, Netrix S.A., Lublin, 2University of Economics and Innovation in Lublin  Poland
Jan Sikora 
Research and Development Center, Netrix S.A., Lublin  Poland