**General Methods**

Some articles deal with general methods which belong to the methodology of chemical process engineering. The article by Furda et al. [1] proposes a general approach, coupling two commercial codes, Aspen Plus® and MATLAB®, for optimal design, with a special focus on energy saving, particularly with regard to steam use, such as in turbines, compressors, pumps and fans. They describe a coupled steam- and process-side modeling approach, taking into account the varying inlet steam parameters or shaft work requirements and implementing fix turbine and driven equipment efficiencies. They even show the influence on the balances. They apply this method to an industrial case of the heat-pump-assisted distillation of a liquid propane–propylene mixture, where the energy for separation is provided by a condensing steam turbine. They propose a serious change due to the increase in high-pressure steam. The impact on the gas emissions is also studied and the economic gains are assessed. Zecevic et al. [2] are also concerned by energy savings applied to a real industrial steam methane reformer unit used for ammonia production. They develop an integrated detailed model which takes into account the production parameters. This enables them to exchange data between the distributed control system and the model to continuously monitor and optimize the steam methane reformer catalyst and tubes. Thus, the overall energy saving is 3% in the real plant. Dizabar et al. [3] compare the exergy and advanced exergy analysis in three different organic Rankine cycles. Exergy is the extent of energy to the second law of thermodynamics by considering entropy and it allows to take into account the irreversibilities in a process. Thus, again, this study focuses on energy saving and process optimization. By advanced exergy analysis, they can separate the exergy loss between unavoidable and avoidable and endogenous and exogenous contributions for each component. This allows them to improve the design by concentrating on the most important components from an energy point of view. Wang et al. [4] propose an efficient numerical method to solve the calculation of a set of nonlinear equations applied to a reactive distillation column and to a distillation column. Their method, called inside-out, divides the calculation into two loop iterations. Sun et al. [5] study the problem of model-based fault diagnosis for a distillation column and, again, this study is strongly related to a numerical and algorithmic point of view. Although it might seem a particular case, their approach is general and can be used for different applications. It consists in adopting a hybrid inverse problem approach using partial least squares to fit and forecast trajectories of the fault parameters generated by least squares. It is applied to the classical Tennessee Eastman process. Skorych et al. [6] study the solution of population balance equations. This type of equation occurs in the description of the distributions of sizes of granular materials, such as polymers and crystals. This is an important problem which often poses numerical difficulties, particularly when it is met in flowsheets and in dynamic problems, resulting in partial differential equations. The authors propose a new model based on transformation matrices. They also use finite volumes to describe the phenomena of agglomeration and breakage. They are able to implement their method in the specially designed Dyssol simulator, which uses a sequential-modular approach. Simulation examples are taken from the pharmaceutical industry. This sufficiently general approach allows the users to apply this method to their own population balance models. Mukhtar et al. [7] study a problem very close to the previous one as it deals with the numerical study of a batch crystallization. They include fines dissolution, which is critical in the industrial practice. Their numerical method is based on a quadrature method of moments. The application refers to test problems, not real problems.
