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Cooling towers are a relatively inexpensive and consistent method of ejecting heat from several industries such as thermal power plants, refineries, and food processing. In this research, an earlier model from literature was to be validated across three different case studies. Unlike previous models, this model considers the height of the fill as the discretised domain, which produces results that give it in a distribution form along the height of the tower. As there are limitations with the software used (gPROMS) where differential equations with respect to independent variables in the numerator and denominator cannot be solved, a derivative of the saturation vapour pressure with respect to the temperature of the air was presented. Results shown were in agreement with the literature and a parametric sensitivity analysis of the cooling tower design and operating parameters were undertaken. In this work the height of fill, mass flowrates of water and air were studied with respect to sensitivity analysis. Results had shown large variations in the outlet temperatures of the water and air if the mass flows of water and air were significantly reduced. However, upon high values of either variable had shown only small gains in the rejection of heat from the water stream. With respect to the height of the fill, at larger heights of the fill, the outlet water temperature had reduced significantly. From a cost perspective, it was found that a change in the water flowrate had incurred the largest cost penalty with a 1 % increase in flowrate had increased the average operating cost by 1.2 %. In comparison, a change in air flowrate where a 1 % increase in flowrate had yielded an average of 0.4 % increase in operating cost.

Microalgae cultivation and their use is a promising approach for integrated CO 2 biofixation, wastewater treatment and renewable energy production. To develop such an important technology, there is a need to optimize the culture conditions, maximizing CO 2 consumption, degrading the nutrients present in the wastewater and maximise the microalgae biomass production. Central Composite Design (CCD) approach was applied to develop quadratic regression models. The developed models were employed separately to estimate optimal sets of three important input parameters (CO 2 concentration, nitrogen-to-phosphorus ratio and culture temperature) for maximizing specific growth rate, biomass productivity and CO 2 biofixation rate. The maximum specific growth rate of 1.93 ± 0.19 d -1 was observed at an optimal set of 34 o C, 4:1 nitrogen-to-phosphorus ratio, and 6 % CO 2 concentration. The maximum biomass productivity of 86.5 ± 20.0 mgL -1 d -1 was obtained at 4.8 % CO 2 , 8:1 nitrogen-to-phosphorus ratio and 28 o C. In addition, the maximum CO 2 biofixation rate was calculated to be 251.9 ± 13.5 mgL -1 d -1 at optimal values of 4 % CO 2 , 1:1 nitrogen-to-phosphorus ratio and 25 o C. Finally, multi-objective optimization method was employed to predict the maximum CO 2 biofixation rate and biomass productivity concurrently. The optimum values of CO 2 biofixation rate (182.84 ± 8.42 mgL -1 d -1 ) and biomass productivity (78.5 ± 10.0 mgL -1 d -1 ) were obtained from operating conditions at 4 % CO 2 , 6:1 nitrogen-to-phosphorus ratio, 25 o C culture temperature. These predicted data were in strong agreement with the experimental values.

This paper presents a novel technique in designing controller for integrating process with inverse response and time delay. Using Pade’s approximation, the positive zero is approximated to a negative zero by modifying the time delay of process. The polynomial approach is employed for the rearranged process to derive the controller parameters. The tuning parameter is selected based on the value of maximum sensitivity. Set point filtering is employed to reduce the overshoot in servo response. Various bench marking examples are considered to evaluate the proposed method. The evaluation is carried out in terms of various performances.

The worldwide growing of gaseous pollutions amount has attracted a great deal of attention for development of clean energy resources like hydrogen. Recently, methanol steam reforming (MSR) has been considered as an effective method for hydrogen production compared to other fuels for reforming. Indeed, advantages of methanol such as its good accessibility and properties like its low boiling point and low probability of coke formation as well as high hydrogen to carbon ratio encourage utilizing this substance in reforming process. Therefore, in this work, MSR as an endothermic reaction has been innovatively coupled with Fischer-Tropsch (FT) exothermic synthesis in order to enhance the yield of hydrogen and gasoline production. Presence of membrane in the proposed thermally coupled membrane reactor (TCMR) promotes H 2 separation as the desired product. A homogeneous one-dimensional steady- state model was considered in the present work. Differential evolution (DE) optimization technique was used to optimize feed molar flow rates and inlet temperatures in both endothermic and exothermic reaction sides with the aim of maximizing gasoline and H 2 yields (in both sides). Results show 42.1 % increase in gasoline yield production and simultaneously high H 2 production yield of 68.5 % in exothermic side compared with the industrial FT reactor that is considered as conventional reactor (CR). Moreover, the suggested configuration can be considered as an energy and cost effective strategy as a result of supplying required energy for endothermic section by generated heat in the exothermic side.

One of the most important processes in the chemical, biological and petrochemical industries is the control of the potential of hydrogen (pH). As it is a multivariable process and non-linear, pH control gives rise to many challenges for designers in both dynamic responses and robustness issues. Despite all this complexity, in many circumstances pH control is performed by using a conventional proportional integral derivative (PID) control, which is very common in industry. This paper proposes using a fractional-order PID to improve the pH control performance of a lab-scale process, as it is more flexible, i. e., there is a higher number of variables to be adjusted. Results from a simulation have been compared to those from both conventional and fractional-order PID controls, which has shown the better performance of the latter related to important metrics such as the control effort and dynamic response of the controlled variables.

The excessive increase in carbon dioxide emissions through the past several decades has raised global climate change concerns. As such, environmental policy makers have been looking into the implementation of efficient strategies that would ultimately reduce greenhouse gas (GHG) emission levels, and meet strict emissions targets. As part of a national emission reduction strategy, the reduction of carbon-dioxide emissions from industrial activities has been proven to be very significant. This instigated the need for a systematic carbon integration approach that can yield cost-effective carbon integration networks, while meeting prescribed carbon dioxide emission reduction targets in industrial cities. A novel carbon integration methodology has been previously proposed as a carbon network source-sink mapping approach using a Mixed Integer Nonlinear Program (MINLP), and was found to be very effective to devise emission control strategies in industrial cities. This paper aims to further improve the design process of carbon integration networks, by coupling carbon integration networks with hydrate suppression/moisture removal options. This was found vital for the prevention of any potential hazards that are associated with the transportation of carbon dioxide in pipelines, such as hydrate formation and various corrosion effects, which may result from moisture retention. An extensive analysis of carbon capture, dehydration, inhibition, compression, and transmission options have all been incorporated into the network design process, in the course of determining cost-optimal solutions for carbon dioxide networks. The proposed approach has been illustrated using an industrial city case study.

The pH neutralization process is typical in chemical, biological and petrochemical industries. One of the major challenges to control it is to understand its nonlinearities and that requires several fine adjustments from conventional controls. Artificial Intelligence has been used to study these nonlinearities; one of them is Neuro-Fuzzy Logic, which was investigated in this work to develop controls dedicated to this process. These controls are formed by logical structures and may be adjusted to different configurations. In practical applications, it is highly important to adapt control parameters based on artificial intelligence to obtain better performance. The present work studied the effect of different configurations of a neuro-fuzzy control on the performance of a regulatory control to pH neutralization process by means of a virtual plant developed in both Indusoft © and Matlab © environments. For both variables, pH and reactor level control, membership function (MF) = [Gaussian], method “OR” = [probabilistic], method “E” = [product], type of MF output = [linear] and the optimization method = [hybrid], have improved control performance, which confirms the importance of configuration choices in neuro-fuzzy control adjustments. Moreover, the most determining factor in NFC performance is the types of membership functions.

In industrial bioprocess control, disturbance sources typically influences process variable regulation. These disturbances may reduce a system control performance or even affect the final bioproduct quality. Therefore, feedforward control is desired because it anticipates the effects caused by these disturbances in an attempt to keep the process variable at the setpoint value. However, designing a feedforward control law requires process modeling, which can be a tough task when dealing with bioprocesses that are intrinsically nonlinear and multivariable systems. Thus, an adaptive feedforward control law or other advanced control system is needed for satisfactory disturbance rejection. For this reason, a general fuzzy feedforward control system is proposed in this paper to replace the classical feedforward control, making it easier to implement the feedforward control action by avoiding nonlinear and multivariable process modeling. The adaptive fuzzy feedforward-feedback (A4FB) system was applied to a product concentration control loop in an enzymatic reactor, to reject disturbances caused by variations in the substrate and enzymatic solutions feed concentration. The results showed that the A4FB controller rejected much more disturbance effects than classical feedforward control law, demonstrating its advantage, supported by not only its simple implementation, but also its improved disturbance rejection.