Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

Advances in Fluid Power Systems

The main purpose of this Special Issue of “Advances in Fluid Power Systems” was to present new scientific work in the field of fluid power systems for hydraulic and pneumatic control of machines and devices used in various industries. Advances in fluid power systems are leading to the creation of new smart devices that can replace tried-and-true solutions from the past. The development work of authors from various research centres has been published. This Special Issue focuses on recent advances and smart solutions for fluid power systems in a wide range of topics, including: • Fluid power for IoT and Industry 4.0: smart fluid power technology, wireless 5G connectivity in fluid power, smart components, and sensors.• Fluid power in the renewable energy sector: hydraulic drivetrains for wind power and for wave and marine current power, and hydraulic systems for solar power. • Hybrid fluid power: hybrid transmissions, energy recovery and accumulation, and energy efficiency of hybrid drives.• Industrial and mobile fluid power: industrial fluid power solutions, mobile fluid power solutions, eand nergy efficiency solutions for fluid power systems.• Environmental aspects of fluid power: hydraulic water control technology, noise and vibration of fluid power components, safety, reliability, fault analysis, and diagnosis of fluid power systems.• Fluid power and mechatronic systems: servo-drive control systems, fluid power drives in manipulators and robots, and fluid power in autonomous solutions

Optimisation of flow chemistry: tools and algorithms

The coupling of flow chemistry with automated laboratory equipment has become increasingly common and used to support the efficient manufacturing of chemicals. A variety of reactors and analytical techniques have been used in such configurations for investigating and optimising the processing conditions of different reactions. However, the integrated reactors used thus far have been constrained to single phase mixing, greatly limiting the scope of reactions for such studies. This thesis presents the development and integration of a millilitre-scale CSTR, the fReactor, that is able to process multiphase flows, thus broadening the range of reactions susceptible of being investigated in this way. Following a thorough review of the literature covering the uses of flow chemistry and lab-scale reactor technology, insights on the design of a temperature-controlled version of the fReactor with an accuracy of ±0.3 ºC capable of cutting waiting times 44% when compared to the previous reactor are given. A demonstration of its use is provided for which the product of a multiphasic reaction is analysed automatically under different reaction conditions according to a sampling plan. Metamodeling and cross-validation techniques are applied to these results, where single and multi-objective optimisations are carried out over the response surface models of different metrics to illustrate different trade-offs between them. The use of such techniques allowed reducing the error incurred by the common least squares polynomial fitting by over 12%. Additionally, a demonstration of the fReactor as a tool for synchrotron X-Ray Diffraction is also carried out by means of successfully assessing the change in polymorph caused by solvent switching, this being the first synchrotron experiment using this sort of device. The remainder of the thesis focuses on applying the same metamodeling and cross-validation techniques used previously, in the optimisation of the design of a miniaturised continuous oscillatory baffled reactor. However, rather than using these techniques with physical experimentation, they are used in conjunction with computational fluid dynamics. This reactor shows a better residence time distribution than its CSTR counterparts. Notably, the effect of the introduction of baffle offsetting in a plate design of the reactor is identified as a key parameter in giving a narrow residence time distribution and good mixing. Under this configuration it is possible to reduce the RTD variance by 45% and increase the mixing efficiency by 60% when compared to the best performing opposing baffles geometry

Modelling and control of waste heat recovery systems for heavy-duty applications

Internal combustion engines (ICEs) are likely to be used in heavy-duty applications for many years and it is important to continue improving their efficiency. Undesirable emissions in internal combustion engines are of major concern due to their negative effect on the human health and global warming. One approach is to recover waste heat from the exhaust of heavy-duty diesel engines (HDDEs) using waste heat recovery (WHR) technologies. WHR based on organic Rankine cycle (ORC) is a promising technology, which offers potential to reduce the fuel consumption of HDDEs by converting the wasted thermal energy to alternative useful electrical or mechanical energy. In the ORC, the evaporator is considered the most critical component of the system. Careful modelling of the evaporator unit is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. This study uses an Adaptive Network-based Fuzzy Inference System (ANFIS) modelling technique to provide efficient control-oriented evaporator models for prediction of heat source and refrigerant temperatures at the evaporator outlet. The ANFIS model benefits from feed-forward output calculation and backpropagation capability of neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using hybrid gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated and the performance of both techniques are compared in terms of RMSE and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both techniques beyond capability of numerical models. However, a better accuracy is achieved for the models trained using the PSO algorithm. Experimentally-measured data is collected from a 1-kWe ORC prototype developed in Clean Energy Processes (CEP) laboratory at Imperial College London and the proposed ANFIS techniques is applied in order to investigate the application of the neuro-fuzzy technique for modelling the evaporator unit. Comparison of the experimental data and the neuro-fuzzy models predictions reveals an acceptable accuracy in predicting the evaporator outlet temperature and pressure. A novel control approach is also proposed to ensure the safe operation of ORC waste heat recovery system and stabilize its work output when subjected to transient heat sources in a range of waste heat from heavy-duty diesel engines. The control strategy comprises a neuro-fuzzy controller based on the inverse dynamics of the ORC system to control the superheating at the evaporator outlet by adjusting the pump speed and a PI controller to maintain the expander work output by regulating the mass flow rate at the expander inlet. The performance of the control strategy is investigated with respect to set-point tracking and its robustness is tested in the presence of noise. The simulation results indicate an enhancement in the controller performance by combination of feedforward and feedback controllers based on neuro-fuzzy techniques. The proposed control scheme not only can obtain satisfactory transient response under various loading conditions, but also can achieve desirable disturbance rejection performance

Systematic Methods for Working Fluid Selection and the Design, Integration and Control of Organic Rankine Cycles—A Review

Efficient power generation from low to medium grade heat is an important challenge to be addressed to ensure a sustainable energy future. Organic Rankine Cycles (ORCs) constitute an important enabling technology and their research and development has emerged as a very active research field over the past decade. Particular focus areas include working fluid selection and cycle design to achieve efficient heat to power conversions for diverse hot fluid streams associated with geothermal, solar or waste heat sources. Recently, a number of approaches have been developed that address the systematic selection of efficient working fluids as well as the design, integration and control of ORCs. This paper presents a review of emerging approaches with a particular emphasis on computer-aided design methods

Advanced Process Control with Applications in the Food Industry

Due to the requirements for enhanced food safety and different nutrient demand for customers, food process control is becoming an increasingly important issue in the food industry. Many advanced control methods, like adaptive control, predictive control, robust control and fuzzy logic control, have attracted increasing attention and there are many successful applications in the manufacture of dairy products in the last two decades. Applying a multi-effect falling film evaporator to remove the water from the liquid is widely used in the dairy industry. This thesis addresses some key issues concerning dairy evaporation process control which include system modelling, controller development and optimization as well as the results comparison. Fundamentally, this thesis presents a study of three effects of falling film evaporator for the milk concentration process in the dairy industry as an example. The main aim, however, is to research, develop and demonstrate different advanced control strategies, such as model predictive control (MPC) and fuzzy logic control (FLC), applied to the evaporator system for the process control purposes. They both can deal with the complex non-linear processes. But MPC can maintain the system consistency, FLC has a simple control structure and more flexiable control rules. A dynamic mathematical model of a three-effect falling film evaporator is developed by using MATLAB based on the mass and energy balance principles in this thesis for analysing the optimization and controllability of the plant. Both conventional and advanced controllers, such as conventional PID, auto-tuning PID, Model predictive control (MPC), Fuzzy logic, are described to maintain and improve the mathematical model performances. The product output concentration controlled by different strategies are obtained and compared. The results indicate that all controllers can achieve the desired targets (30%, 38% and 52% for the 1st, 2nd, and 3rd effect) within limits of acceptability, however, MPC is the most competitive advanced control strategy in this milk evaporation process

10th EASN International Conference on Innovation in Aviation & Space to the Satisfaction of the European Citizens

This Special Issue book contains selected papers from works presented at the 10th EASN (European Aeronautics Science Network) International Conference on Innovation in Aviation & Space, which was held from the 2nd until the 4th of September, 2020. About 350 remote participants contributed to a high-level scientific gathering providing some of the latest research results on the topic, as well as some of the latest relevant technological advancements. Eleven interesting articles, which cover a wide range of topics including characterization, analysis and design, as well as numerical simulation, are contained in this Special Issue

Catalytic Performance of Al-HDTMA Bentonite Impregnated Fe on Phenol Hydroxylation

This paper describes the addition of Fe into modified-bentonite layer by impregnation method. Natural bentonite (from Pacitan, Indonesia) was intercalated with HDTMA-Br 1,5 % solution before pillared with Al metal to give Al-HDTMA bentonite forms. The ratio of bentonite and intercalating agent or pillaring agent was 1 gr/50 ml. The mixture was agitated, and then the solid phase was washed with distilled water. Then it was dried and calcined at 723 K for 4 hours. This modified bentonite was then impregnated with Fe solution. The Fe concentrations were 0.01 M, 0.05 M, and 0.1 M. All the materials were characterized using FT-IR and X-ray diffraction. X-ray diffractogram showed that the higher Fe penetrates into bentonite, the lower the crystallinity of bentonite. Their catalytic activity and selectivity were studied for phenol hydroxylation using H2O2 (30%). The reaction conditions of this reaction were as follows: ratio of phenol/ H2O2 = 1:1 (molar ratio), concentration of phenol = 1 M, reaction temperatures were 333 K, and ratio of catalyst/phenol was 1:10. The best catalytic performance to convert phenol and produce hydroquinone by phenol hydroxylation reaction is on PILB HDTMA-Al,Fe 0.05 M

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Plantwide Control and Simulation of Sulfur-Iodine Thermochemical Cycle Process for Hydrogen Production

A PWC structure has developed for an industrial scale SITC plant. Based on the performance evaluation, it has been shown that the SITC plant developed via the proposed modified SOC structure can produce satisfactory performance – smooth and reliable operation. The SITC plant is capable of achieving a thermal efficiency of 69%, which is the highest attainable value so far. It is worth noting that the proposed SITC design is viable on the grounds of economic and controllability