We start with a review of several studies where the obtained in the drying industry with the use of control tools are explicitly stated:

• For grain dryer ,an optimization procedure allowed to significantly decrease the drying cost by 33.6% from 2.29 £/t to 1.52 £/t, with the optimal tuning of the air flows 30% less than normal .The optimal tuning for the heater powder became 85% less than normal
• For a grain dryer ,the use of an on-line optimal controller led to an 17% decrease in drying time ,a 6.4%decrease in fuel consumption and a total cost decrease of 1.3%
• In a beet sugar factory,the use of a model based predictive controller reduced the energy costs by 1.2% (18,900 £/year)and decresed the downstream energy cost by14,000 £/year.The product yield increase by 0.86% worth 61,600 £/year and off-specification production has decresed from 1%to 4% .Finally ,the payback time (including hardware and development ) was 17months.
• For a rotary dryer ,a PI controller was used to control the outlet mean product moisture  content near the desired set-point .The throughput was increased by 1.4%leading to a potential increase of a company profit by 105,845US$/year.Concerning energy consumption ,it decreased bt 7% from 22,069 Kw (manual control ) to 20,483 Kw (qutomatic control ) .Based on an 80,000 Us $ control system ,the payback time (including hardware and development) was 9months.

Clearly ,one can see that the use of control tools allows to improve benefits and to decrease energy consumpyion and off- specification production .Moreover ,ecen if the initial control study is not simple (e.g.:in case of both first principle model and software developments) return on this investment is relatively short as it does not exceed 1.5years.