Low-temperature, reactivity-controlled compression ignition (RCCI) combustion has proven instrumental in resolving the long-standing trade-off between engine emissions and efficiency, particularly for heavy-duty applications. However, RCCI has an inherent sensitivity to variations in-cylinder charge composition, such as fuel stratification, temperature gradients, and air-fuel mixing. This makes combustion behavior unpredictable and difficult to regulate using conventional control methods. This study presents an advanced multivariable model-based control design (MBCD) toolchain tailored for marine RCCI engines. Specifically, it introduces a real-time adaptive model predictive control (AMPC) strategy to regulate the indicated mean effective pressure (IMEP) and the crank angle at 50% mass fraction burned (CA50) by manipulating the total fuel energy and the blend ratio (BR) between the two fuels. The control framework is evaluated through model-in-the-loop (MiL) simulations with an experimentally validated high-fidelity UVATZ (University of Vaasa Advanced Thermo-Kinetic Multi-zone) model of a W & auml;rtsil & auml; 31DF engine combustor as the plant, and a physics-based linear real-time model (RTM) as an observer. The controller's performance is benchmarked against a decentralized PI controller under various transient scenarios. Both controllers achieve comparable tracking of IMEP and CA50, but the AMPC demonstrates faster IMEP response (within eight cycles), lower CA50 steady-state error (maximum 0.45 crank-angle degree (CAD)), and reduced fuel consumption (2.7%). Additionally, AMPC's receding-horizon framework and self-tuning features enhance robustness against unstructured uncertainties and parameter variations, marking a significant advancement over previously proposed predictive control strategies.

The increase of popularity of reactivity-controlled compression ignition (RCCI) is attributed to its capability of achieving ultra-low nitrogen oxides (NOx) and soot emissions with high brake thermal efficiency (BTE). The complex and nonlinear nature of the RCCI combustion makes it challenging for model-based control design. In this work, a model-based control system is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of RCCI combustion through the adjustments of total fuel energy and blend ratio (BR) in fuel injection. A physics-based nonlinear control-oriented model (COM) is developed to predict the main combustion performance indicators of an RCCI marine engine. The model is validated against a detailed thermo-kinetic multizone model. A novel linear parameter-varying (LPV) model coupled with a model predictive controller (MPC) is utilized to control the aforementioned parameters of the developed COM. The developed system is able to control combustion phasing and IMEP with a tracking error that is within a 5% error margin for nominal and transient engine operating conditions. The developed control system promotes the adoption of RCCI combustion in commercial marine engines.

Model-based design is proven to be essential for the development of control systems. This paper presents a real-time predictive control-orientated model (COM) for low-temperature combustion (LTC), dual-fuel, reactivity-controlled compression ignition (RCCI) engines. A comprehensive model-based design methodology must be capable of constructing an RCCI control-orientated model with high accuracy, high noise immunity, good response, predictivity in governing mechanisms, and low computation time. This work attains all of these for the first time for a cutting-edge RCCI marine engine. The real-time model (RTM) captures the key sensitivities of RCCI by controlling the total fuel energy and the blend ratio (BR) of two fuels, while also considering uncertainties arising from variations of inlet temperature and the gas exchange process. It provides not only the cycle-wise combustion indicators but also the crank-angle-based cylinder pressure trend. The RTM is derived by direct linearisation of a physics-based model and is successfully validated against experimental results from a large-bore, RCCI engine and the previously acknowledged UVATZ (University of Vaasa Advanced Thermo-kinetic Multi-zone) model. Validation covers both steady-state and transient modes. With high accuracy in several case studies representing typical load transients and air-path disturbance rejection tests, the model predicts maximum cylinder pressure (Pmax), crank-angle of 5 % burnt (CA5), crank-angle of 50 % burnt (CA50) and indicated mean effective pressure (IMEP) with root means square (RMS) errors of 8.6 %, 0.3 %, 0.6 %, and 0.6 % respectively. The average simulation time without any code optimisation is around 5 ms/cycle, offering sufficient real-time surplus to incorporate a semi-predictive emission submodel within the current approach.

Reactivity controlled compression ignition (RCCI) technology has gained in popularity due to its ability to achieve low level NOx and soot emissions with relatively high brake thermal efficiency. However, control of RCCI combustion is a complex task. Nonetheless, this challenge can be overcome with model-based control design (MBCD). In this work, a linear physics-based time-varying RCCI combustion model was developed and improved with an addition of a start-of-combustion (SOC) model. The model we developed, which is capable of real-time simulations, can predict the combustion phasing, heat-release, and cylinder pressure of an RCCI marine engine. The model showcases the trend-wise, high accuracy estimation of cumulative heat-release and cylinder pressure. Additionally, it is able to predict combustion phasing parameters with an error of less than 1% for control design.

The increasing demand of lowering the emissions of the combustion engines has led to the development of more complex engine systems. This paper presents artificial neural network (ANN) based models for estimating nitrogen oxide (NOx) and carbon dioxide (CO2) emissions from in-cylinder pressure of a maritime diesel engine. The architecture of the models is that of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) network. The data utilized to train and test the models are obtained from a four-cylinder marine engine. The inputs of the models are chosen as the first principal components of the in-cylinder pressure and engine parameters with highest correlation to aforementioned greenhouse gases. Generalization is performed on the models during the training to avoid overfitting. The estimation result of each model is then compared. Additionally, contribution of each cylinder to the production of emissions is investigated. Results indicate that MLP has a higher accuracy in estimating both NOx and CO2 compared to RBF network. The emission levels of each cylinder for both NOx and CO2 are mostly even due to the nature of the conventional diesel engine.

In this work neural network models are used to reconstruct incylinder pressure from a vibration signal measured from the engine surface by a low-cost accelerometer.Using accelerometers to capture engine combustion is a cost-effective approach due to their low price and flexibility.The paper describes a virtual sensor that re-constructs the in-cylinder pressure and some of its key parameters by using the engine vibration data as input.The vibration and cylinder pressure data have been processed before the neural network model training.Additionally, the correlation between the vibration and in-cylinder pressure data is analyzed to show that the vibration signal is a good input to model the cylinder pressure.The approach is validated on a RON95 single cylinder research engine realizing homogeneous charge compression ignition (HCCI).The experimental matrix covers multiple load/rpm steady-state operating points with different start of injection and lambda setpoints.A radial basis function (RBF) neural network model was first trained with a series of two operating points at low loads with data of 1000 consecutive combustion cycles, to build the needed nonlinear mapping.The results show that the developed neural network model is capable of reconstructing in-cylinder pressure at low loads with good accuracy.The error for combustion parameter such as maximum cylinder pressure did not exceed 5%.The approach is further validated with another series of operating points consisting of both low loads and high loads.However, the results in this case deteriorated.Changing the neural network model to generalized regression (GR) improved the incylinder pressure reconstruction quality.The performance of the models was also considered in terms of combustion parameters, such as maximum pressure and mass burned fraction.The paper concludes that vibration signal carries sufficient information to estimate combustion parameters independently on the engine platform or combustion concept.

This short remark describes some of the key points of the Floquet-Lyapunov theory of linear periodic differential systems.The basic transformation is presented, and its properties from the point of view of the source and target matrices and stability are discussed.The proposed methodology can be used in analysis, control design and simulation of systems with time-periodic characteristics.A method for designing a stabilizing state feedback control law is proposed.The methodology can be used in analysis and controller design for example in processes involving rotating machines and engines.

The increasing demands for reducing fuel consumption and emissions in contemporary technology solutions lead to the use of more sensors, actuators, and control applications. With this increasing engine complexity, the feedback design is complex due to the coupling between inputs and combustion parameters. To be able to design the controller systematically, model predictive control (MPC) comes to the scope because of its advantages in the design of multi-input multi-output (MIMO) systems, especially with its constraints handling ability and performance in simultaneously optimizing the engine fuel efficiency and emission reduction. Multi-injection is one of the promising techniques for achieving better engine performance. In this work, post-injection control is implemented utilizing MPC MIMO strategy with the target of exploring the possibility of reducing emissions and improving engine efficiency by controlling post-injection duration and injection timing. The workflow of the MPC controller design from control-oriented model (COM) establishing to MPC problem formation and solution methodology is discussed in this work. Moreover, one contribution from this work is the different implementation angle when compared with the state-of-the-art approaches, where the MPC controller is implemented purely by Matlab Simulink to enable the rapid control prototyping design. The simulation result demonstrated the ability of the controller's tracking performance and showed a preliminary step towards the nonlinear combustion model-based multi-injection MPC design. The systematic model-based controller framework developed in this work can be applied to other control applications and enables a fast path from design to test.

In this work, a model predictive controller is developed for a multiple injection combustion model. A 1D engine model with three distinct injections is used to generate data for identifying the state-space representation of the engine model. This state-space model is then used to design a controller for controlling the start of injection and injected fuel mass of the post injection. These parameters are used as inputs for the engine model to control the maximum cylinder pressure and indicated mean effective pressure.

Present study investigates the end-use performance of alternative liquid fuels in the current fleet of unmodified light-duty vehicle (LDV) engines. Two mathematical models have been developed that represent the way that various fuel properties affect fuel consumption in spark-ignition (SI) and compression-ignition (CI) engines. Fuel consumption is represented by the results from the New European Driving Cycles (NEDC) in order to reflect the end-use impact. Data-driven black-box modeling and multilinear regression methods were applied to obtain both models. Additionally, quantitative analysis was performed to ensure the statistical significance of inputs (p-value below 5%). Fuel consumption (output) of various alternative fuels can be estimated with high accuracy (coefficient of determination above 0.96), knowing fuel properties (inputs) such as lower heating value, density, cetane/octane number, and oxygen content. The validation procedures confirmed the quality of predictions for both models with the average error being below 2.3%. The model performance for the examined fuels such as hydrotreated vegetable oil (HVO) and ethanol blends showed significant CO2 reduction with high accuracy. Moreover, both models could be used to estimate CO2 tailpipe emissions and are applicable to various liquid SI/CI fuels for LDV engines.

This paper presents an approach to a new engine calibration method that takes the engine's operational profile into account. This method has two main steps: modeling and optimization. The Design of Experiments method is first conducted to model the engine's responses such as Brake Specific Fuel Consumption (BSFC) and Nitrogen Oxide (NOx) emissions as the functions of fuel injection timing, common rail pressure and charged air pressure. These response surface models are then used to minimize the fuel consumption during a year, according to a typical load profile of a ferry, and to fulfill the NOx limits set by International Maritime Organization (IMO) regulations, Tier II, test cycle E2. The Sequential Quadratic Programming algorithm is used to solve this minimization problem. The results showed that the fuel consumption can be effectively reduced with the flexibility to trade it off with the NOx emissions while still fulfilling the IMO regulations. In general, this method can decrease the manual calibration effort and improve the engine's performance with a tailored setting for individual operational profiles.

Novel combustion concepts and multi-injection cylinder-wise control methods are needed in large marine diesel engines for increased performance and to reduce the green house gas emissions. Even though diesel technology in cars might be reducing there is no replacement of dual fuel diesel technology in large marine engines to be seen in the near future. The paper discusses a rapid grey-box modeling technique, which can be used to predict cylinder pressure and heat release in engine cylinders. The model can be used to design effective cylinder-wise control algorithms which increase the engine performance and save fuel under constraint of emissions.

The objective of this paper is to design static optimal control maps of diesel engines to achieve high efficiency and emission reduction. The calibration tool applied to create the control maps, named "Off-line parameterization tool", was designed based on the Design of Experiments method. The optimization goal is to minimize the Brake Specific Fuel Consumption (BSFC) by the engine's input parameters under emission constraints. The tool was designed to work both fully automatically and semi-automatically. Many reports on engine calibration have taken the Design of Experiments (DoE) approach, but their implementations in choosing experimental design types and optimization processes are different compared to this paper. The unique aspect of this research lies in the significant properties of the Off-line parameterization tool. First, this tool is flexible: it is able to work with multiple inputs and multiple outputs. Second, it can reduce the calibration time as the engine running time is kept as short as possible and all the data processing work is accomplished automatically.

Marine transportation sector is highly dependent on fossil-based energy carriers.Decarbonization of shipping can be accomplished by implementing biobunkers into an existing maritime fuel supply chain.However, there are many compatibility issues when blending new biocomponents with their fossil-based counterparts.Thus, it is of high importance to predict the effect of fuel properties on marine engine performance, especially for new fuel blends.In the given work, possible future solutions concentrated on liquid fuels are taken into account.Under consideration are such fuels as biodiesel (FAME), hydrotreated vegetable oil (HVO), straight vegetable oil (SVO), pyrolysis oil, biocrude, and methanol.Knowledge about the behavior of new fuel in an existing engine is notably important for decision makers and fuel producers.Hence, the main goal of the present work is to create a model, which can predict the engine performance from the end-user perspective.For the purpose of modeling, only the latest research on marine fuels is taken into account.In the current approach, results from a representative measurement set-up are compared in order to create a uniform model.As a result, all the provided data are expressed in relative changes in reference to standard marine fuel -heavy fuel oil (HFO).The modeling Is performed by means of multilinear regression and accuracy of the model is relatively high, with a coefficient of determination over 0.9.The outcomes provide a prediction of final engine performance for the specified fuel blend.Knowing the final properties of fuel (such as calorific value, density, viscosity), it is attainable to estimate fuel consumption, carbon dioxide emissions and determine possible fuel compatibility issues.Moreover, the model enables estimation of carbon dioxide (CO 2 ) tailpipe emissions, which should be included in the whole Life Cycle Analysis (LCA) while assessing the renewability index of the fuel.

T he present-day transport sector needs sustainable energy solutions. Substitution of fossil-fuels with fuels produced from biomass is one of the most relevant solutions for the sector. Nevertheless, bringing biofuels into the market is associated with many challenges that policy-makers, feedstock suppliers, fuel producers, and engine manufacturers need to overcome. The main objective of this research is an investigation of the impact of alternative fuel properties on light vehicle engine performance and greenhouse gases (GHG). The purpose of the present study is to provide decision-makers with tools that will accelerate the implementation of biofuels into the market. As a result, two models were developed, that represent the impact of fuel properties on engine performance in a uniform and reliable way but also with very high accuracy (coefficients of determination over 0.95) and from the end-user point of view. The inputs of the model are represented by fuel properties, whereas output by fuel consumption (FC). The parameters are represented as percentage changes relative to standard fossil fuel, which is gasoline for spark ignition (SI) engines and diesel for compression ignition (CI) engines. The methodology is based on data-driven black-box modeling (input-output relation). The multilinear regression was performed using the data from driving cycles such as the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and New European Driving Conditions (NEDC). The FC of SI engines proved to be dependent on mass-based Net Calorific Value (NCV), Research Octane Number (RON), oxygen content and density. However, CI engines performance is affected by NCV, density and Cetane Number (CN). The models were additionally subject to quantitative analysis, where input parameters in both models turned out to be statistically significant (p-value below 5%). Additionally, the validation stage consisted of residual analysis confirmed the accuracy of both models. The GHG part estimates the change of carbon dioxide emissions based on fuel consumption, which represents the tailpipe emissions.

Impedance-ratio-based interaction analyses in terms of stability and performance of DC-DC converters is well established. Similar methods are applied to grid-connected three-phase converters as well, but the multivariable nature of the converters and the grid makes these analyses very complex. This paper surveys the state of the interaction analyses in the grid-connected three-phase converters, which are used in renewable-energy applications. The surveys show clearly that the impedance-ratio-based stability assessment are usually performed neglecting the cross-couplings between the impedance elements for reducing the complexity of the analyses. In addition, the interactions, which affect the transient performance, are not treated usually at all due to the missing of the corresponding analytic formulations. This paper introduces the missing formulations as well as explicitly showing that the cross-couplings of the impedance elements have to be taken into account for the stability assessment to be valid. In addition, this paper shows that the most accurate stability information can be obtained by means of the determinant related to the associated multivariable impedance ratio. The theoretical findings are also validated by extensive experimental measurements.

In this paper, inspired by the herding behavior of rhinos, a new kind of swarm-based metaheuristic search method, namely Rhino Herd (RH), is proposed for solving global continuous optimization problems.In various studies of rhinos in nature, the synoptic model is used to describe rhino's space use and estimate its probability of occurrence within a given domain.The number of rhinos increases year by year, and this increment can be forecasted by several population size updating models.Synoptic model and a population size updating model are formalized and generalized to a general-purpose metaheuristic optimization algorithm.In RH, null model without introducing any influences is generated as the initial herding.This is followed by rhino modification via synoptic model.After that, the population size is updated by a certain population size updating model, and newly-generated rhinos are randomly initialized within the given conditions.RH is benchmarked by fifteen test problems in comparison with biogeography-based optimization (BBO) and stud genetic algorithm (SGA).The results clearly show the superiority of RH in searching for the better function values on most benchmark problems over BBO and SGA.

The paper discusses the changes and challenges in the current teaching of Automatic Control systems. Modern society has developed into a phase where the traditional process industry is not at all the only area where dynamic modelling, understanding the feedback, control engineering, autonomous systems and generally the discipline of Automatic control have to be mastered. That gives a huge challenge to the teaching of automatic control in general, especially when fewer and fewer students are entering engineering schools and as the basic skills in mathematics and physics seem to be decreasing everywhere. On the other hand, automation (to be understood broadly including automatic control and control engineering, autonomous systems etc.) as a discipline is in a state of change: it seems to be hidden in other engineering fields, and there seems to be opinions that it should actually be taught within specific application areas, e.g. in electrical engineering, machine design, chemical process engineering etc. In the old school of control engineering the idea is actually vice versa: automatic control is seen as a general, mathematically and physically well-defined discipline, which can the be applied in various application areas and engineering fields. The societal and industrial viewpoints must both be considered, when looking at the future of control education. These aspects are discussed in the paper.

The increase in intermittent renewable generation motivates participation of also the electricity consumers in maintaining the production-consumption balance of the electrical grid. This paper proposes a distributed price-based optimization scheme for involving a population of consumers in day-ahead procurement of electricity and frequency containment reserves. Energy storage charging and heating of residential houses are planned before the operating day, while taking into account uncertainties in the day-ahead electricity and reserves markets, weather conditions, and realized frequency. The proposed approach is formulated as stochastic quadratic programming problems and coordinated using a parallelized formulation of the alternating direction method of multipliers (ADMM). Chance constraints for the storage state of charge variations under uncertain reserve activation are formulated as second-order cone constraints. The numerical results illustrate the potential benefits of including consumers in the optimization of reserves.

The Flower Pollination Algorithm (FPA) is a new natural bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination.Thus, the latter has a quick convergence, but its population diversity and convergence precision can be limited in some applications.In order to improve its intensification (exploitation) and diversification (exploration) abilities, we have introduced a simple modification in its general structure.More precisely, we have added both Crossover and Mutation Genetic Algorithm (GA) operators respectively, just after calculating the new candidate solutions and the greedy selection operation in its basic structure.The proposed method, called FPA-GA has been tested on all the CEC2005 contest test instances.Experimental results show that FPA-GA is very competitive.