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Morad Danishvar is a senior research fellow in System Engineering Research Group at Brunel University London, UK. He received his BSc degree in Electronic Engineering, the MSc. degree in Engineering Management and the PhD degree from Brunel University London in 2015. His research interest is in Data Science, Data analytics and machine learning, AI, real-time systems modelling and optimization. Morad has worked from 2017-2020 on the EU Horizon 2020 Z-Factor project which focuses on zero defects in manufacturing systems. And currently is a senior research fellow on the EU Horizon 2020 DEEP PURPLE. He is a member of IET.
Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant.
Morad Danishvar; Sebelan Danishvar; Francisco Souza; Pedro Sousa; Alireza Mousavi. Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model. Applied Sciences 2021, 11, 1361 .
AMA StyleMorad Danishvar, Sebelan Danishvar, Francisco Souza, Pedro Sousa, Alireza Mousavi. Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model. Applied Sciences. 2021; 11 (4):1361.
Chicago/Turabian StyleMorad Danishvar; Sebelan Danishvar; Francisco Souza; Pedro Sousa; Alireza Mousavi. 2021. "Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model." Applied Sciences 11, no. 4: 1361.
An improved method for the real time sensitivity analysis in large scale complex systems is proposed in this paper. The method borrows principles from the event tracking of interrelated causal events and deploys clustering methods to automatically measure the relevance and contribution made by each input event data (ED) on system outputs. The ethos of the proposed event modeling (EM) technique is that the behavior or the state of a system is a function of the knowledge acquired about events occurring in the system and its wider operational environment. As such it builds on the theoretical and the practical foundation for the engineering of knowledge and data in modern and complex systems. The proposed EM platform EventiC filters noncontributory ED sources and has the potential to include information that was initially thought irrelevant or simply not considered at the design stage. The real-time ability to group and rank relevant input-output ED in order of its importance and relevance will not only improve the data quality, but leads to an improved higher level of mathematical formulization in the modern complex systems. The contribution of the approach to systems' modeling is in the automation of data analysis, control, and plant process modeling. EventiC has been validated as the monitoring and the control system for a cement factory. In addition to the previously known parameters, the proposed EventiC identified new influential parameters that were previously unknown. It also filtered 18% of the input data without compromising the data quality or the integrity. The solution has improved the quality of input variable selection and simplify plant control strategies.
Morad Danishvar; Alireza Mousavi; Peter Broomhead. EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2018, 50, 1649 -1662.
AMA StyleMorad Danishvar, Alireza Mousavi, Peter Broomhead. EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018; 50 (5):1649-1662.
Chicago/Turabian StyleMorad Danishvar; Alireza Mousavi; Peter Broomhead. 2018. "EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 5: 1649-1662.