Band 18, Heft 4

An effective tuning methodology of modified Smith predictor (MSP) based fractional controller designing for purely integrating time delayed (IPTD) processes is reported here. IPTD processes with pole at the origin are truly difficult to control; exhibit large oscillations once get disturbed from their steady state. Proposed MSP design consists of fractional PI (proportional-integral) and fractional PD (proportional-derivative) controllers together with P (proportional) controller. Fractional controllers are competent to provide improved closed loop responses due to flexibility of additional tuning parameters. Fractional tuning parameters of PI and PD controllers are derived through optimization algorithms where integral absolute error (IAE) is considered as cost function. Efficacy of the proposed methodology is validated for IPTD processes having wide range of time delay. Stability and robustness issues are explored under process model uncertainties with small gain theorem. Performance of the proposed MSP-FO(PI–PD) controller is validated through simulation study relating five IPTD process models. Overall satisfactory closed loop responses are observed for each case during transient as well as steady state operational phases.

This study deals with the problem of estimating the amount of biomass and lactic acid concentration in a lactic acid production process. A continuous stirred tank bioreactor was used for the culture of Lactobacillus helveticus . A nonlinear sliding mode observer is proposed and designed, which gives an estimate of both the biomass and lactic acid concentrations as a function of glucose uptake from the culture medium. Numerical results are given to illustrate the effectiveness of the proposed observer against a standard sliding-mode observer. It was found that the proposed observer worked very well for the benchmark bioreactor model. Also, the numerical results indicated that the proposed estimation methodology was robust to the uncertainties associated with un-modelled dynamics. These new sensing technologies, when coupled to software models, improve performance for smart process control, monitoring, and prediction.

Cream cheese, a popular condiment, is widely used in people’s daily diet and in dessert making. To ensure high-quality cream cheese production, the pH value is generally used as the indicator to determine the end point of cream cheese fermentation. The inoculation time and time-dependent concentrations of biomass, lactose, lactic acid are all crucial for pH prediction. However, the inoculation time could vary for industrial applications with multiple fermenters. Moreover, the inoculation time impact on fermentation has not been investigated. This paper aims to build a cream cheese fermentation model predicting pH. The model includes a semi-batch kinetic model and an artificial neural network (ANN) model. The outcome of the model will help the cream cheese industries understand the inoculation time impact on fermentation time and organise better fermenter scheduling.

In this work, the modeling of flow equations and associated transported phenomena in wetted-wire columns (WWC) has been carried out by using the CFD method. The studied processes in this column included the absorption of H 2 S and CO 2 gases from the gas stream by absorbent solution. In this regard, laboratory results were available only for CO 2 absorption in a column with a single wire or 109 wires. Moreover, the fact that modeling of a wetted-wire column needs robust hardware. As a result, firstly, the process of CO 2 absorption with monoethanolamine (MEA) solution in a column with a wire was modeled by COMSOL Multiphysics version 5.6. Then, the results of various parameters were compared with laboratory results (the error percentage was calculated to be 2.4%). It was observed that by increasing the liquid flow rate, the distance between the beads decreased and beads with larger diameters and higher velocities formed. Meanwhile, for the first time, the temperature profile inside the column was determined along the column, the temperature of the liquid phase increased. The gas stream after a slight increase in temperature, left the column with a temperature close to the incoming liquid. After model validation, other processes were investigated, resulting from changing desired gas for separation or liquid solution. Finally, different absorbents’ abilities were predicted to absorb gaseous pollutants and obtained that in terms of absorption efficiency, second-type alkanolamines perform better than other types in the simultaneous absorption of CO 2 and H 2 S.

In this work, we developed a facile method for photocatalytic deposition of cobalt atoms as co-catalyst on TiO 2 nanosheets (Co( x )/ T ) under visible light instead of UV irradiation for the first time. The deposition of cobalt atoms on TNs in the Co( x )/ T samples was confirmed by DRS, Raman spectroscopy, photoluminescence, nitrogen physisorption, and TEM analyses. The size of cobalt nanoparticles/cluster dispersed on the TiO 2 nanosheets were in the range of 5–20 nm according to TEM results. The PXRD patterns showed that the crystal structure and the anatase phase of TNs were preserved in the Co( x )/ T samples after the visible light-assisted deposition process. The Co( x )/ T samples showed higher activity compared to pure TiO 2 nanosheets for the visible light degradation of tetracycline (TC) as pharmaceutical pollutant due to presence of cobalt co-catalyst. We studied the effect of several parameters on the degradation process and proposed the mechanism of degradation according to quenching experiments results. Due to time-consuming and costly of experimental works, we designed two strong artificial intelligence (AI) models (artificial neural networks (ANN) and neuro-fuzzy inference systems (ANFIS)) to estimate the removal process of TC, and predict the removal percent of TC for new values of inputs before performing experiment. The experimental and computational studies showed that the fabricated photocatalysts are as promising candidates for industrial wastewater treatment to meet environmental regulations and provide a new avenue for practical implications.

A numerical simulation through computational fluid dynamics is presented on the fluid flow mixing in a flow-focusing microfluidic device with three inlet channels confluence angles of 45, 67.5, and 90°. The effect of various parameters such as aspect ratio (0.5, 1, and 1.5), mixing channel length (1–4 mm), and Reynolds number (1–20) on the mixing efficiency, and the pressure drop are evaluated. The results demonstrate that the increase in mixing efficiency results from an increase in the Reynolds number and aspect ratio for all the angles. In addition, an increase in the pressure drop due to an increase in the Reynolds number and a decrease in the aspect ratio is observed. A longer length of the mixing channel indicates a higher mixing efficiency. The mixing efficiency is more suitable at an angle of 45° among the applied angles in terms of the operational and geometric parameters due to an increase in the contact surface of the flows at the inlet channels junction since the mixing index range is between 0.54 and 1 by varying the mentioned parameters.

Vegetable oils are a crucial source of raw materials for many industries. In order to meet the rising demand for oil on global scale, it has become essential to investigate underutilized plant oilseeds. Hura crepitans seeds are one of the underused plant oilseeds from which oil can be produced via solvent-based extraction. For the purpose of predicting the oil yield from Hura crepitans seeds, the extraction process was modelled using a nonlinear autoregressive exogenous neural network (NARX-NN). The input variables to the model are seed/solvent ratio, extraction temperature and extraction time, while oil yield is the response output variable. NARX-NN model is based on 200 data samples, and model architecture comprises of 3 inputs, 1 hidden layer (with 15 neurons) and 1 output with 2 delay elements. The performance evaluation was carried out to examine the accuracy of the developed model in predicting oil yield from Hura crepitans using different statistical indices. The overall correlation coefficient, R (0.80829), mean square error, MSE (0.0120), root mean square error, RMSE (0.1080) and standard prediction error, SEP (0.1666) show that NARX-NN model can accurately be used for the prediction oil yield from Hura crepitans seeds.

In this research work, intensifying the possibility of protonated orange peel to uptake thorium (IV) ions from aqueous solutions in a batch system was investigated and optimized using the response surface methodology. The effect of three independent process variables including thorium initial concentration, pH, and biosorbent dosage was assessed based on the central composite design. The validity of the quadratic model was verified by the coefficient of determination. The optimization results showed that the rate of thorium (IV) uptake under optimal conditions is 183.95 mg/g. The modeling results showed that the experimental data of thorium biosorption kinetics are fitted well by the pseudo-second-order model. According to the results, the biosorption process reached equilibrium after around 4 h of contact. The Langmuir isotherm describes the experimental biosorption equilibrium data well. The maximum absorption capacity of protonated orange peel for thorium adsorption was estimated by the Langmuir isotherm at 236.97 mg/g. Thermodynamic studies show that thorium adsorption on protonated orange peel is thermodynamically feasible, spontaneous, and endothermic.

Despite steam sterilisation in autoclaves being a common industrial method of sterilisation, very little research has been conducted into quantifying the resources these processes demand and their associated environmental impacts. This paper aims to investigate industrial steam sterilisation in autoclaves with particular application to the biopharmaceutical industry. A mathematical model of a steam autoclave was developed to examine relationships between load size, load material properties and autoclave capacity with energy consumption, environmental impact and cost of sterilisation. The two main energy requirements are thermal energy to produce the clean steam for sterilising, and electrical energy for the vacuum pump. The study showed that thermal energy is dominant, particularly as load increases. The percentage of the maximum load at which the autoclave is operated has a major impact on the specific energy requirement or the energy required to sterilise per unit mass of load. For a given autoclave, the energy requirement increases with increased load but the specific energy requirement decreases. This in turn impacts on the emissions and the energy cost. It is thus shown that it is much more energy efficient to operate at higher loads, making the autoclave much more energy and cost effective, and with less environmental impact. There is potential for applying the analysis presented in this work for conducting optimisation studies for determining the sizes of autoclaves that could minimise the energy requirement, environmental impact and economic cost (3E) of investments for specified load versus time profiles.

Molecular dynamics simulations are used to explore the wetting behavior of a water-ethanol droplet on the silicon surface. The effect of ethanol concentration on the wettability of a water-ethanol droplet on the silicon surface was analysed by calculation of contact angle. At 30% ethanol concentrations, the water contact angle was 50.7°, while at 50% ethanol concentrations, it was 36°. The results showed that the contact angle of a droplet on a silicon surface decreases with increasing ethanol concentrations. The formation of hydrogen bonds (HBs) between water-water molecules was 677 for the 30% ethanol system, while at 50% ethanol concentrations, it was 141. The number of hydrogen bonds between water molecules reduces as the ethanol concentrations rise. The HBs between water molecules and the silicon surface is seen to grow as the ethanol concentration rises. The overall potential energies of pure water, 7:3 water-ethanol, and 1:1 water-ethanol systems are −74.4, −96.16, and −158.59 kcal/mol, respectively. The contact angle and number density of water molecules on the surface of the silicon revealed that at different ethanol concentrations, more water molecules are distributed on the silicon surface.