Volume 17, Issue 1

The processes which contain at least one pole at the origin are known as integrating systems. The process output varies continuously with time at certain speed when they are disturbed from the equilibrium operating point by any environment disturbance/change in input conditions and thus they are considered as non-self-regulating. In most occasions this phenomenon is very disadvantageous and dangerous. Therefore it is always a challenging task to efficient control such kind of processes. Depending upon the number of poles present at the origin and also on the location of other poles in transfer function different types of integrating systems exist. Stable first order plus time delay systems with an integrator (FOPTDI), unstable first order plus time delay systems with an integrator (UFOPTDI), pure integrating plus time delay (PIPTD) systems and double integrating plus time delay (DIPTD) systems are the classifications of integrating systems. By using a well-controlled positioning stage the advances in micro and nano metrology are inevitable in order satisfy the need to maintain the product quality of miniaturized components. As proportional-integral-derivative (PID) controllers are very simple to tune, easy to understand and robust in control they are widely implemented in many of the chemical process industries. In industries this PID control is the most common control algorithm used and also this has been universally accepted in industrial control. In a wide range of operating conditions the popularity of PID controllers can be attributed partly to their robust performance and partly to their functional simplicity which allows engineers to operate them in a simple, straight forward manner. One of the accepted control algorithms by the process industries is the PID control. However, in order to accomplish high precision positioning performance and to build a robust controller tuning of the key parameters in a PID controller is most inevitable. Therefore, for PID controllers many tuning methods are proposed. the main factors that lead to lifetime reduction in gain loss of PID parameters are described in This paper and also the main methods used for gain tuning based on optimization approach analysis is reviewed. The advantages and disadvantages of each one are outlined and some future directions for research are analyzed.

Due to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO 2 to SO 3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives.

Due to the finite source of fossil fuels and their high emissions, it is remarkable to recognize appropriate ways to produce alternative fuels with less pollution. In this paper, the production of biodiesel (fatty acid methyl ester) from transesterification of methanol with sesame oil under ultrasound-assisted waves (using a homogeneous sodium hydroxide catalyst) was investigated. In addition, the optimization and prediction of biodiesel production was studied and compared with the two methods of response surface methodology (RSM) and the combined model of artificial neural network (ANN) – particle swarm algorithm (PSO). The central composite design (CCD) was used to investigate the effect of independent variables (methanol/oil molar ratio, catalyst percentage, reaction time and temperature) on the yield of biodiesel in Expert Design software. Analysis of experimental results was performed using RSM and ANN–PSO hybrid methods and also the optimal conditions for maximizing the yield were calculated. The highest yield of biodiesel predicted by RSM and ANN–PSO were 87.4 and 90.58%, respectively. RSM and ANN–PSO hybrid models were compared based on least squared errors statistically. The correlation coefficients in the RSM and ANN–PSO hybrid models were 0.959 and 0.999 respectively. While both models demonstrated a good agreement with actual results, but the ANN–PSO hybrid model had a powerful prediction for the optimal points over the RSM.

Methanol is an essential chemical building block for the synthesis of numerous industrial products, and has the potential of becoming an alternative fuel. In this study, a simulation of methanol production process was carried out using Aspen Plus software. The process involves two stages, namely syngas production through the dry reforming of methane (DRM) in a reformer reactor and the actual methanol production by the conversion of the syngas obtained. Plug reactor unit operation was employed for the conversion of syngas from the DRM reactor to methanol. Thereafter, the influence of various operating parameters including DRM temperature, plug reactor specification temperature, and pressure effects was studied via the model analysis tool. A rundown of the optimal conditions obtained are DRM temperature of 1050 °C for better conversion of feed and minimal carbon deposit, CH 4 /CO 2 ratio of 0.71, plug reactor constant temperature of 198 °C for optimum methanol yield (4600 kmol) for the given gaseous feed flow rates (5000 kmol/h methane and 7000 kmol/h CO 2 ).

Desalination of nitrates from brackish water is prominent in the coastal areas due to excessive disposal of pesticides by agricultural industries. Nowadays, membrane processes are growing tremendously for the desalination of brackish water. In this context, polyurea (PU) could be a useful membrane material for the treatment of brackish water. The present work deals with the removal of nitrates from synthetic water using PU membranes by nanofiltration (NF) process. Polyurea thin film composite (PU-TFC) membranes were prepared by interfacial polymerization followed by thermal crosslinking and characterized using Fourier transformed infrared spectral (FTIR), X-ray diffraction (XRD), scanning electron microscopy– energy dispersion X-ray spectroscopy (SEM–EDS), Atomic force microscopy (AFM), thermogravimetric (TGA), and universal testing machine (UTM) for structural analysis, crystallinity, morphological, compositional, thermal and mechanical properties, respectively. Experimental studies were conducted on an NF pilot plant by varying operating pressure from 2 to 10 bar and feed nitrate concentration from 60 to 200 mg/L for evaluating PU membrane performance. Experimental observations revealed a maximum water flux of 30.6 L/m 2  h and nitrate rejection of 97.2% at a pressure of 10 bar for feed containing 140 mg/L of nitrate. A mass transfer model was developed on the basis of solution–diffusion mechanism for a semi-batch NF process by considering cake enhanced concentration polarization model, for laminar flow with feed recycle, using a plate and frame membrane module. A generic semi-batch NF process model was integrated taking into account concentration polarization and fouling layer resistance. The integrated model was successfully compared with existing data in literature and could be used for process scale-up. Due to the merits of hydrophilicity, negative charge, high thermal and mechanical resistance, the PU membrane can be termed as a low cost, commercially viable and ecofriendly barrier for separation of nitrates.