The heat recovery system (HRS) is a vital part in a paper machine when it comes to the overall energy economy in papermaking. For a typical newsprint machine more than 60% of the exhaust energy from the dryer section can be recovered, resulting typically in a regain of about 30 MW. The synthesis task of a HRS is a decision process where the target is on the one hand to achieve maximal energy recovery and on the other hand to obtain it with minimal investment costs. These goals are contradictory and thus the problem is to find a solution minimizing the overall costs, considering simultaneously both energy and investment costs. One further challenge is how to take into account known variations in the parameters so that the design is robust, i.e. it is capable of handling all evolving situations and furthermore the design should be the most economical one when also considering the duration of all expected operational situations. In this work a hybrid method based on evolutionary programming and non-linear programming for the synthesis of robust and optimal heat recovery systems (HRS) for paper machines is presented. Variations and uncertainties on process parameters are modeled with probability distributions and the cost of the utilities are thus obtained as an integrated value of all different situations expected to evolve. The importance of obtaining a robust design is shown in a case study. (c) 2007 Elsevier B.V. All rights reserved.