Optimizing Economic Load Dispatch with Renewable Energy Sources via Differential Evolution Immunized Ant Colony Optimization Technique

Recently, renewable energy (RE) has become a trend in power generation. It is slowly evolving from an alternative energy source into the main energy source. The technology is currently working as an auxiliary to the existing generators. Demands for electricity is expanding rapidly nowadays, which require generators to run near its operation limit. This activity put grieve risk to the generators. Nonetheless, the extensive analysis should be conducted upon RE integration into the existing power system. This paper assesses its economic impact on the power system. Setting up RE technology such as photovoltaic and wind turbine are costly, yet may reduce generator’s fuel cost in the long run. Thus, economic load dispatch (ELD) is conducted to compute the operating cost of power system with the integration of RE system. In this study, the operating cost represents the fuel cost of conventional fossil-fuel generators. Furthermore, a novel optimization technique namely Differential Evolution Immunized Ant Colony Optimization is proposed as the optimization engine. Comparative studies are conducted to assess the performance of the proposed approach

Orthonormal functions based model predictive control of pH neutralization process

U radu se opisuje primjena Legendre i Laguerre ortonormalnih funkcija za predstavljanje upravljačke putanje u prediktivnom upravljanju diskretnim modelom, kod pH procesa neutralizacije, procesa sa statičnom nelinearnošću. Provedba je testirana uz postojanje nepodešenosti između postrojenja i modela. Ortonormalne funkcije su rabljene za učinkovitu parametrizaciju razlike upravljačkog signala kao u slučaju linearnog procesa. U ovom je pristupu računalna učinkovitost bolja u usporedbi s klasičnim algoritmima prediktivnog upravljanja.This paper presents use of Legendre and Laguerre orthonormal functions for representation of the control trajectory in discrete model predictive control, on a pH neutralization process, which is a process with static non-linearity. Performance is tested with plant-to-model mismatch present. Orthonormal functions are used for efficient parameterisation of the difference of control signal as in the case of linear process. This approach has better computational efficiency compared to the classical predictive control algorithms

Process analytical technology in food biotechnology

Biotechnology is an area where precision and reproducibility are vital. This is due to the fact that products are often in form of food, pharmaceutical or cosmetic products and therefore very close to the human being. To avoid human error during the production or the evaluation of the quality of a product and to increase the optimal utilization of raw materials, a very high amount of automation is desired. Tools in the food and chemical industry that aim to reach this degree of higher automation are summarized in an initiative called Process Analytical Technology (PAT). Within the scope of the PAT, is to provide new measurement technologies for the purpose of closed loop control in biotechnological processes. These processes are the most demanding processes in regards of control issues due to their very often biological rate-determining component. Most important for an automation attempt is deep process knowledge, which can only be achieved via appropriate measurements. These measurements can either be carried out directly, measuring a crucial physical value, or if not accessible either due to the lack of technology or a complicated sample state, via a soft-sensor.Even after several years the ideal aim of the PAT initiative is not fully implemented in the industry and in many production processes. On the one hand a lot effort still needs to be put into the development of more general algorithms which are more easy to implement and especially more reliable. On the other hand, not all the available advances in this field are employed yet. The potential users seem to stick to approved methods and show certain reservations towards new technologies.Die Biotechnologie ist ein Wissenschaftsbereich, in dem hohe Genauigkeit und Wiederholbarkeit eine wichtige Rolle spielen. Dies ist der Tatsache geschuldet, dass die hergestellten Produkte sehr oft den Bereichen Nahrungsmitteln, Pharmazeutika oder Kosmetik angehöhren und daher besonders den Menschen beeinflussen. Um den menschlichen Fehler bei der Produktion zu vermeiden, die Qualität eines Produktes zu sichern und die optimale Verwertung der Rohmaterialen zu gewährleisten, wird ein besonders hohes Maß an Automation angestrebt. Die Werkzeuge, die in der Nahrungsmittel- und chemischen Industrie hierfür zum Einsatz kommen, werden in der Process Analytical Technology (PAT) Initiative zusammengefasst. Ziel der PAT ist die Entwicklung zuverlässiger neuer Methoden, um Prozesse zu beschreiben und eine automatische Regelungsstrategie zu realisieren. Biotechnologische Prozesse gehören hierbei zu den aufwändigsten Regelungsaufgaben, da in den meisten Fällen eine biologische Komponente der entscheidende Faktor ist. Entscheidend für eine erfolgreiche Regelungsstrategie ist ein hohes Maß an Prozessverständnis. Dieses kann entweder durch eine direkte Messung der entscheidenden physikalischen, chemischen oder biologischen Größen gewonnen werden oder durch einen SoftSensor. Zusammengefasst zeigt sich, dass das finale Ziel der PAT Initiative auch nach einigen Jahren des Propagierens weder komplett in der Industrie noch bei vielen Produktionsprozessen angekommen ist. Auf der einen Seite liegt dies mit Sicherheit an der Tatsache, dass noch viel Arbeit in die Generalisierung von Algorithmen gesteckt werden muss. Diese müsse einfacher zu implementieren und vor allem noch zuverlässiger in der Funktionsweise sein. Auf der anderen Seite wurden jedoch auch Algorithmen, Regelungsstrategien und eigne Ansätze für einen neuartigen Sensor sowie einen Soft-Sensors vorgestellt, die großes Potential zeigen. Nicht zuletzt müssen die möglichen Anwender neue Strategien einsetzen und Vorbehalte gegenüber unbekannten Technologien ablegen

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Prediction interval-based modelling and control of nonlinear processes

 Novel computational intelligence-based methods have been investigated to quantify uncertainties prevalent in the operation of chemical plants. A new family of predication interval-based controlling algorithms is proposed and successfully applied to chemical reactors in order to minimise energy consumption and operational cost

Linear Parameter-varying Models For Predictive Control Design: Application To Nonlinear Chemical Reactors Thiago Vaz Da Costa

This paper presents the application of an identification algorithm based on local model networks able to split the full model dynamics in linear parameter-varying (LPV) models for different regions on the process operating range. It is shown that a model based controller equipped with an efficient LPV model performs better than when a single linear time-invariant (LTI) model is used. Results demonstrated that model adaptation over several regions provides better system representation leading to more efficient and consistent control in already implemented control loops.212219Ozkan, L., Kothare, M.V., Georgakis, C., Control of a solution copolymerization reactor using multi-model predictive control (2003) Chemical Engineering Science, 58 (7), pp. 1207-1221. , DOI 10.1016/S0009-2509(02)00559-6, PII S0009250902005596Babuska, R., Verbruggen, H., Neuro-fuzzy methods for nonlinear system identification (2003) Annual Reviews in Control, 27 (1), pp. 73-85Causa, J., Karer, G., Núñez, A., Sáez, D., Škrjanc, I., Zupančič, B., Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor (2008) Computers and Chemical Engineering, 32 (12), pp. 3254-3263Ljung, L., (1999) System Identification: Theory for the User, , 2nd ed., Prentice Hall, New JerseyNelles, O., (2001) Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models, , Berlin, SpringerHafner, M., Schüler, M., Nelles, O., Isermann, R., Fast neural networks for diesel engine control design (2000) Control Engineering Practice, 8 (11), pp. 1211-1221Nelles, O., Isermann, R., Basis function networks for interpolation of local linear models (1996) Proc. of the 35th IEEE Conference on Decision & Control, pp. 470-475. , Kobe, JapanRossiter, J., (2003) Model-based Predictive Control: A Practical Approach, , Florida, CRC PressFischer, M., Nelles, O., Isermann, R., Predictive control based on local linear fuzzy models (1998) International Journal of Systems Science, 29 (7), pp. 679-697Espinosa, J., Vandewalle, J., Predictive control using fuzzy models (1998) Advances in Soft Computing: Engineering Design and Manufacturing, 8 (11), pp. 187-200. , Springer-VerlagBlazic, S., Skrjanc, I., Design and stability analysis of fuzzy model-based predictive control - A case study (2007) Journal of Intelligent and Robotic Systems: Theory and Applications, 49 (3), pp. 279-292. , DOI 10.1007/s10846-007-9147-8Khairy, M., Elshafei, A., Emara, H., LMI based design of constrained fuzzy predictive control (2010) Fuzzy Sets and Systems, 161 (6), pp. 893-918Bequette, B.W., Non-linear model predictive control: A personal retrospective (2007) Canadian Journal of Chemical Engineering, 85 (4), pp. 408-415Boling, J.M., Seborg, D.E., Hespanha, J.P., Multi-model adaptive control of a simulated pH neutralization process (2007) Control Engineering Practice, 15 (6), pp. 663-672. , DOI 10.1016/j.conengprac.2006.11.008, PII S0967066106002188, Special Section on Control Applications in Marine SystemsMahmoodi, S., Poshtan, J., Jahed-Motlagh, M., Montazeri, A., Nonlinear model predictive control of a pH neutralization process based on wiener-laguerre model (2009) Chemical Engineering Journal, 146 (3), pp. 328-337Marusak, P., Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors (2009) Applied Soft Computing, 9 (3), pp. 1111-1125Henson, M., Seborg, D., Adaptive nonlinear control of a pH neutralization process (1994) IEEE Transactions on Control Systems Technology, 2 (3), pp. 169-182Gustafsson Tore, K., Waller Kurt, V., Dynamic modeling and reaction invariant control of pH (1983) Chemical Engineering Science, 38 (3), pp. 389-398. , DOI 10.1016/0009-2509(83)80157-