Path Planning Methods in Chemical Engineering

Edvards Valbahs, Peter Grabusts, Ilo Dreyer


Usually, when the practical motion planning and the shortest path are discoursed, mainly the limited number of tasks is observed. Almost all the tasks associated with the path from one point in 2D or 3D space to another point can be attributed to the usual issue in the practical application. Motion planning and the shortest path have vivid and indisputable importance as human activity in such areas as logistics and robotics. In our work we would like to draw particular attention to the field of application seems to be unnoticeable for the task such as motion planning and the shortest path problem. Due to quite simple examples used, we would like to show that the task of motion planning can be used for simulation and optimization of multi-staged and restricted processes which are presented in chemical engineering accordingly. In the article the simulation and optimization of three important chemical-technological processes for the chemical industry are discussed. The work done gave us the possibility to work out software for simulation and optimization of processes that in some cases facilitates and simplifies the work of professionals engaged in the field of chemical engineering.


chemical industry; path planning; Travelling salesman problem; Simulated Annealing

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R. Azencott, Simulated annealing: parallelization techniques. John Wiley and Sons, 1992.

W.J. Cook, In Pursuit of the Traveling Salesman. Princeton: Princeton University Press, USA, 2011.

D. D. Davendra, Traveling Salesman Problem, Theory and Applications. Rijeka: InTech, 2010.

P. Patnaik, Handbook of Inorganic Chemicals. McGraw-Hill, 2003.

E. Aarts and J. Korst, Simulated annealing and Boltzmann machines: A stochastic approach to combinatorial optimization and neural computing. John Wiley and Sons, 1989.

N.N. Greenwood and A. Earnshaw, Chemistry of the Elements. 2nd ed., Elsevier - Butterworth–Heinemann, 1997.

R. H. J. M. Otten and L. P. P. P. Ginneken, The Annealing Algorithm. Kluwer Academic Publishers, 1989.

G. Towler and R. Sinnott, Chemical Engineering Design, Second Edition: Principles, Practice and Economics of Plant and Process Design. 2nd ed., Butterworth–Heinemann, 2012.

D. Voet, J.G. Voet and C.W. Pratt, Principles of Biochemistry. 4th ed., John Wiley & Sons, Inc., 2012.

D.L. Applegate, R. Bixby, V. Chvátal and W. Cook, The Traveling Salesman Problem: A Computational Study. Princeton University Press, 2006.

F. A. Cotton, G. Wilkinson, C.A. Murillo and M. Bochmann, M, Advanced Inorganic Chemistry. 6th ed., John Wiley & Sons: New York, 1999.

E. Valbahs and P. Grabusts, Path Planning Using Non - Euclidean Metric, 8th EUROSIM Congress on Modelling and Simulation EUROSIM2013, Cardiff, Wales, United Kingdom, 10-12 September, 173-178, 2013.

R. Agarwala, D.L. Applegate, D. Maglott, G.D. Schuler and A.A. Schäffer, “A Fast and Scalable Radiation Hybrid Map Construction and Integration Strategy,” Genome Research, 10, 350–364, 2000.

R.G. Bland and D.F. Shallcross, “Large traveling salesman problems arising from experiments in x-ray crystallography,” Operations Research Letters, 8, 123-133, 1989.

S. Carlson, “The Amateur Scientist: Telescopes and a problem for traveling salesmen in space,” Scientific American, 276, 121 – 123, 1997.

S. Climer and W. Zhang, “Rearrangement clustering: Pitfalls, remedies, and applications,” Journal of Machine Learning Research, 7, 919–943,2006.

M.A. Fox and K. Wade, “Evolving patterns in boron cluster chemistry,” Pure Appl. Chem., 75 (9), 1315–1323, 2003.

V. Granville, M. Krivanek and J-P. Rasson, “Simulated annealing: A proof of convergence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 652–656, 1994.

M. Grötschel, M. Jünger and G. Reinelt, “Optimal Control of Plotting and Drilling Machines: A Case Study,” Zeitschrift für Operations Research, 35(1), 61-84, 1991.

L. Ingber, “Simulated annealing: Practice versus theory,” Math. Comput. Modelling, 18, 29–57, 1993.

S. Kirkpatrick, C.D. Gelatt and M.P. Vecchi, “Optimization by Simulated Annealing,” Science, vol. 220, 671-680, 1983.

E. Kolemen and N.J. Kasdin, “Optimal Trajectory Control of an Occulter-Based Planet-Finding Telescope,” Advances in Astronautical Sciences, AAS 07-037, Vol. 128, 215- 233, 2007.

J.K. Lenstra, “Clustering a data array and the traveling salesman problem,” Operations Research, 22, 413–414, 1974.

N. Weiner, “Malonic acid. Organic Syntheses,” Coll. Vol. 2, 376, 1943.

“Robot scientist Eve will allow scientists to save time and money in the development of new drugs,” [Online]. Available: [Accessed: February 23, 2015].



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