Design and robust optimization of a novel industrial continuous heat treatment furnace

This paper designs and robustly schedules a novel AAHP (air to air heat pump) based ICHTF (industrial continuous heat treatment furnace) which is driven by a solar Stirling engine and a wind turbine. The proposed flame-making process equipped with two inside and outside air fans, an expansion valve, compressor, condenser, and an evaporator to supply the heating demand with the high-temperature more than 1000 K.The heating energy obtained from the AAHP is not produced during the combustion reactions but is transferred from the ambient air to the furnace chamber. Therefore, use of solar, wind and AAHP in an industrial furnace with no need to fossil fuels not only reduces the stack pollutant emissions and ozone-depleting substances, but also increases the economic savings in the consumptions of petroleum products and electricity, significantly. In this study, an energy-exergy based mathematical framework is comprehensively presented to model the proposed ICHTF in the presence of renewable energy sources such as air, solar, and wind. Moreover, an interval robust economic optimization strategy is introduced to minimize total energy procurement cost using the forecasted wind speed as inputs and taking into account the upper and lower limits of the wind power for modeling its uncertainties. In the robust model, the range of wind power is divided into several consecutive nested subintervals. The proposed ICHTF is simulated on a benchmark small-scale industrial sector which is located in a tropical region in order to minimize total electricity cost considering the operational constraints of Stirling cycle and AAHP considering the wind generation uncertainties. Simulation results reveal the feasibility and strength of the proposed ICHTF by solving a RNLP (robust nonlinear programming) problem using the CONOPT solver under GAMS (general algebraic mathematical system) environment.