Optimal Set-point Research Articles

Optimal model-based control for automated robotized abrasive blasting system

In robotic abrasive blasting operations, achieving the required surface roughness and cleanliness relies heavily on manually pre-set operational parameters. These parameters, such as stand-off distance, blasting angle, inlet air pressure, abrasive flow rate, and particle size, are set based on operator experience. However, given that optimal values for these parameters vary depending on specific blasting conditions and surface requirements, the pre-set values often fail to deliver both the desired surface quality and productivity. To address these challenges, we propose a solution employing a set of proxy models and a model predictive control system to achieve optimal operational outputs. For energy efficiency and productivity maximization, we utilize computational fluid dynamics with a multiphase flow solver to determine the optimal stand-off and offset distances, thus controlling the blasting nozzle effectively. Additionally, we build a data-driven model to obtain the optimal reference set point of air pressure at the sensor location based on the desired surface roughness. Moreover, we develop a dynamic process model to link the inlet air pressure and abrasive flow rate to the air pressure at the sensor location, which facilitates the development of a model-based control system. By applying this comprehensive approach, we evaluate and optimize the control and operational parameters to achieve the desired surface roughness and productivity targets. To validate the effectiveness of our newly developed control system and models, extensive tests are conducted both virtually (in silico) and on-site. The results confirm the stability and accuracy of the control system. In on-site reliability tests, the automated blasting system successfully delivers the desired surface roughness with a relative error of less than 5 % and a remarkable 30–50 % improvement in productivity. Overall, our innovative control system and models offer a reliable and efficient solution for robotic abrasive blasting, ensuring consistent surface quality and productivity enhancements.

Does Maslow’s Hierarchy of Needs Explain Volunteer Time Allocations? An Exploration of Motivational Time Allowances Using the American Time Use Survey

This study examines whether there is an optimal set point along Maslow’s (1943) hierarchy of needs associated with disaggregated forms of volunteerism. Taniguchi’s (2012) study examined major life domains (e.g., work, education, and religion) for associations with formal and informal volunteerism. An alternative approach is to include life domains with variables measuring motivational concerns to better identify time allocation patterns for disaggregated volunteerism. This study analyzed the results of 9,435 time diaries recorded on the 2019 American Time Use Survey (ATUS). The time allocated to formal and informal volunteerism associates with intermediate belongingness concerns. There is no association between time spent on self-esteem and self-actualization concerns and informal volunteering. Tertiary education as a baseline measure for self-actualization shares a weaker association than belonging with formal volunteering. The data suggest that research into maximizing formal volunteerism may be searching at the wrong point at self-actualization. Implications are discussed for motivating volunteerism.

Data-Driven Optimal Control for Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is a dynamic industrial process involving complex physical and chemical reactions. Due to the uncertain municipal solid waste (MSW) composition and dynamic operation conditions, it is difficult to guarantee optimal operation for the MSWI process. To solve this problem, a data-driven optimal control scheme is proposed with a hierarchical control structure. In the set points optimization stage, an online adaptive fuzzy neural network (OAFNN) is designed to construct the objective functions, including combustion efficiency and NOx emission concentration. An adaptive mutation particle swarm optimization (AMPSO) algorithm is developed to obtain the optimal set points of oxygen content in flue gas. In the control stage, a double long short-term memory neural networks-based model predictive control (DLSTM-MPC) strategy is exploited. A self-organizing long short-term memory (SOLSTM) network is employed to predict the oxygen content in flue gas with a compact structure. To reduce the influences from dynamic disturbances and further improve the tracking control performance, another long short-term memory (LSTM) neural network is established to correct the prediction results. Finally, the set points optimization method and DLSTM-MPC strategy are combined to realize the optimal operation of the MSWI process. The effectiveness of the proposed optimal control scheme is verified by real industrial data. The experimental results demonstrate that the optimal control scheme can achieve promising tracking control performance of oxygen content in flue gas and improve the operational performance of the MSWI process.

Quantifying Energy Savings from Optimal Selection of HVAC Temperature Setpoints and Setbacks across Diverse Occupancy Rates and Patterns

With the advent of flexible working arrangements, we are observing a dramatic shift in how buildings are occupied today, which presents an opportunity to optimize Heating, Ventilation, and Air Conditioning system temperature setpoints based on variations in occupancy. Guidelines often suggest the adoption of the highest or lowest setpoint or setback to minimize energy consumption in hot or cold climates, respectively. However, at outdoor temperatures where variations in occupancy heat loads prompt buildings to fluctuate across cooling, free-running, and heating mode, optimal setpoints and setbacks are not always the lowest or highest. In addition, the perturbations caused by rapid switching between setpoint and setback could diminish energy savings due to system destabilization. This paper aims to systematically compare the potential energy savings from fixed and optimal setpoints and setbacks across wide-ranging occupancy scenarios (four occupancy rates and 14 patterns). Energy simulations were conducted using the Department of Energy reference models for small, medium, and large office buildings to enable an exhaustive search of optimal setpoint/setbacks in 17 climate zones. Explored setpoints were 19.5 °C to 25.5 °C with intervals of 1 °C, and setbacks were 17 °C/19 °C for heating and 26 °C/28 °C for cooling. The findings indicate that, on average, while lower occupancy heat loads results in 5.48% energy reduction, a conventional fixed setpoint and setback strategy provides an additional 11.80%, and optimal selection of setpoints and setbacks could provide an additional 34.36–38.08%, emphasizing the untapped potential energy saving. To facilitate practical applications, this paper presents an interactive graphical interface: Optimal Temperature Setpoint Tool.

Enhancing energy efficiency and comfort with a multi-domain approach: Development of a novel human thermoregulatory model for occupant-centric control

Occupant Centric Control (OCC) strategies aims to achieve a more personalized and comfortable indoor environment, translating occupants' comfort requirements into real environmental conditions. This strategy relies on a multilevel non-linear programming optimization procedure to determine optimal thermo-hygrometric setpoints. The objective is to minimize space heating and cooling requirements while addressing multi-domain comfort concerns related to individual thermal sensations and perceived air quality. To facilitate this process, this study adopts a novel Personal Comfort Model (PCM), incorporating physiological factors to predict occupants' thermal sensations and CO2 intakes. The PCM's reliable predictive capabilities are confirmed through validation with real experimental data from a test room at the University of Perugia. For detailed energy analyses, considering occupants' subjective comfort preferences, the PCM is seamlessly integrated into DETECt, an in-house building energy performance simulation tool developed by the University of Naples Federico II, for the design of advanced control algorithms and energy efficiency strategies. To effectively manage the HVAC system, a model predictive control is implemented, using the determined setpoints to ensure mechanical ventilation and maximize cost savings. The proof of concept for the developed methodology involves simulating experimental tests using the thermal model of the human body and the new facility management system, to be simulated and optimized by means of the enhanced building energy performance simulation tool, exploited for the design and operation of more occupant-centric and sustainable buildings. The proposed study demonstrates that through the developed OCC strategy enables a significant reduction in thermal discomfort (40 % and 60 % less than the occupation time for test 1 and test 2, respectively) compared to reference scenarios. Additionally, energy analysis reveals high efficiency, achieving savings of 12.8 % and 7.8 % of electricity consumed for HVAC system operation.

Control of bidirectional prosumer substations in smart thermal grids: A weighted proportional-integral control approach

In future smart thermal grids, decentralized prosumers shall supply each other over the network, optimized by an overarching management system. For this, the substation control must be able to directly process optimal power flow setpoints from the management, while considering the mutual influence of prosumers on the technical level. Addressing these requirements, this paper develops a control approach for bidirectional prosumer substations. Therefore, we identify the objectives and characteristics of the control problem, before relaxing it by reformulation. This enables to keep proportional–integral–derivative controllers (PIDs) for operating the pumps and valves as decentral actuators in the substations. We propose a control approach, exploring the combination of two key aspects: (1) The assignment of temperature objectives to actuators. (2) Weighted error functions that linearly combine temperature and power errors as input for the PIDs, allowing to handle multiple objectives with limited actuators. Through case studies, we validate the suitability of our proposed objective–actuator assignment and demonstrate the necessity and benefits of the weighted error functions. Like this, we conclude with a control approach for bidirectional prosumer substations that aims for the relevant temperature objectives, directly considers heat transfer setpoints, replicates and exploits the coupling within the network, effectively manages conflicting objectives, copes with prosumer interference, and is pragmatic for real-time operation. Our approach can be transferred to different network concepts, bridging the gap between decentralized field-level operation and the management level.

Hierarchical bi-level load frequency control for multi-area interconnected power systems

To overcome the shortcomings of proportional–integral (PI) control, a linear quadratic regulator (LQR) is proposed in this study, to determine the set points of the PI controllers in a two-level hierarchical manner. The application of the proposed scheme is studied on load frequency control (LFC) of a power system with multiple control areas (CAs). At the lower control level, a discrete PI controllers are used to regulate the frequency and tie-line powers of each of the CAs. To achieve an improved closed loop performance, the local PI controllers are optimally tuned using moth flame optimization (MFO) algorithm by minimizing the Integral Square Error (ISE) of the state errors with respect to local information and interactions with neighboring CAs. While at the upper control layer, an optimized finite-horizon LQR is applied as a guide to establish the optimal set-points of the lower level PI controllers. Since only few of the state variables can be accessed, Kalman filter (KF) is applied as an observer for the estimation of the remaining states. The efficacy of the proposed scheme is verified by implementing it on a three-CA system perturbed with a step and random net disturbances formed by combining the fluctuations in the output power of DFIG-based wind turbine generator and random load demand. From the simulation results, it is established that the proposed LQR-PI supervisory scheme has outperformed the conventional PI control scheme in optimality and stability.

Co-simulation-based optimal reactive power control in smart distribution network

The increasing integration of distributed energy resources such as photovoltaic (PV) systems into distribution networks introduces intermittent and variable power, leading to high voltage fluctuations. High PV integration can also result in increased terminal voltage of the network during periods of high PV generation and low load consumption. These problems can be solved by optimal utilization of the reactive power capability of a smart inverter. However, solving the optimization problem using a detailed mathematical model of the distribution network may be time-consuming. Due to this, the optimization process may not be fast enough to incorporate this rapid fluctuation when implemented in real-time optimization. To address these issues, this paper proposes a co-simulation-based optimization approach for optimal reactive power control in smart inverters. By utilizing co-simulation, the need for detailed mathematical modeling of the power flow equation of the distribution network in the optimization model is eliminated, thereby enabling faster optimization. This paper compares three optimization algorithms (improved harmony search, simplicial homology global optimization, and differential evolution) using models developed in OpenDSS and DigSilent PowerFactory. The results demonstrate the suitability of the proposed co-simulation-based optimization for obtaining optimal setpoints for reactive power control, minimizing total power loss in distribution networks with high PV integration. This research paper contributes to efficient and practical solutions for modeling optimal control problems in future distribution networks.

Intelligent Setpoint Optimization for a Range Extender Hybrid Electric Vehicle with Opposed Piston Engine

Abstract This paper presents the exploration and optimization of a hybridized opposed piston (OP) engine. In this work, the exhaust crankshaft lead (ECL) is introduced as a controllable parameter in the hybridized OP engine enabled by eliminating the conventional geartrain linking the two crankshafts of an OP engine. This allows for variation in the effective compression and expansion ratio of the engine, along with scavenging performance. This novel control actuator as well as the adjustable speed and load setpoint in a series hybrid OP engine powertrain architecture necessitates an intensive calibration effort to realize any possible efficiency improvements. However, the OP engine within this series hybrid powertrain does not need to operate in highly transient conditions, but rather its operating point is fixed or slowly varying. This property permits using online calibration techniques. After manually sweeping speed and ECL values at two power setpoints, the use of an extremum seeking type inter-cycle optimization algorithm to optimize the operating setpoint is validated, showing that near optimal speed and ECL setpoints can be selected despite the relatively flat operating map of the OP engine.

A data-driven method for the optimal control of centralized cooling station in an office park

An effective way to reduce the energy consumption of a building is to optimize the control strategy for the HVAC system. Load prediction is suggested and used to match the supply and demand for air conditioning and achieve energy savings. However, the gap between load prediction models and real-time optimal control of HVAC systems still exists. Hence, this paper proposed an optimization method for dynamically determining the best setpoints of chillers and chilled water pumps under a specific load. The energy consumption model of each equipment in the centralized cooling station is established and validated using the operational data. Then an optimization problem is defined to find the optimal setpoints for each equipment under certain load, to realize the lowest energy consumption. To verify the validity of the proposed method, a period of real operational data in an office park is used. The proposed method is applied on one centralized cooling station in the office park and results in an 4% lower overall energy consumption than the existing intelligent control strategies in the park. This method provides feasible directions and reference for realizing overall optimal control of the whole HVAC system in the future.

Improving the Performance of a Hierarchical Traffic Flow Control Framework Using Lyapunov-Based Switched Newton Extremum Seeking

Abstract The primary aim of this research paper is to enhance the effectiveness of a two-level infrastructure-based control framework utilized for traffic management in expansive networks. The lower-level controller adjusts vehicle velocities to achieve the desired density determined by the upper-level controller. The upper-level controller employs a novel Lyapunov-based switched Newton extremum seeking control approach to ascertain the optimal vehicle density in congested cells where downstream bottlenecks are unknown, even in the presence of disturbances in the model. Unlike gradient-based approaches, the Newton algorithm eliminates the need for the unknown Hessian matrix, allowing for user-assignable convergence rates. The Lyapunov-based switched approach also ensures asymptotic convergence to the optimal set point. Simulation results demonstrate that the proposed approach, combining Newton’s method with user-assignable convergence rates and a Lyapunov-based switch, outperforms gradient-based extremum seeking in the hierarchical control framework.

Deep reinforcement learning for fuel cost optimization in district heating

This study delves into the application of deep reinforcement learning (DRL) frameworks for optimizing setpoints in district heating systems, which experience hourly fluctuations in air temperature, customer demand, and fuel prices. The potential for energy conservation and cost reduction through setpoint optimization, involving adjustments to supply temperature and thermal energy storage utilization, is significant. However, the inherent nonlinear complexities of the system render conventional manual methods ineffective. To address these challenges, we introduce a novel learning framework with an expert knowledge module tailored for DRL techniques. The framework leverages system status information to facilitate learning. The training is performed by employing model-free DRL methods and a refined digital twin of the Espoo district heating system. The expert module, accounting for power plant capacities, ensures actionable directives aligned with operational feasibility. Empirical validation through comprehensive simulations demonstrates the efficacy of the proposed approach. Comparative analyses against manual methods and evolutionary techniques highlight the approach's superior ability to curtail fuel costs. This study advances the understanding of DRL in district heating optimization, offering a promising avenue for enhanced energy efficiency and cost savings.

Active disturbance rejection control: an application to continuous microalgae photobioreactors

AbstractBACKGROUNDMathematical modelling is a widely employed approach for investigating the growth behaviour of microalgae. As a result, the development of model‐based controllers to regulate process variables has garnered increasing attention. However, despite the significant efforts invested in this area, control performance can be adversely affected by unmodelled dynamics and disturbances.RESULTSTwo active disturbance rejection controllers (ADRC) were designed to enable robust tracking of biomass concentration in continuous microalgae photobioreactors, with reduced reliance on the mathematical model of the system. The controllers were tuned to achieve a nonovershoot response and minimize settling time based on the culture's characteristics. Simulations were performed using optimal setpoints specific to each model. The results showcased a maximum output signal deviation of ±2.2%, ±7.8% and ± 7.62% for the Dunaliella tertiolecta, Isochrysis affinis galbana and Chlorella vulgaris models, respectively, regardless of the presence of simulated disturbances.CONCLUSIONSThe findings of this study significantly contribute to the advancement of the field of sustainable microalgae production. By introducing less dependent model‐based controllers, this research enhances the feasibility of implementing robust control strategies. These controllers require only knowledge of the equation system's order and the control gain function, simplifying the design process. This approach effectively addresses control performance degradation arising from unmodelled dynamics and disturbances. The ability to maintain desired process variables through ADRC controllers not only ensures improved control performance, but also supports the cultivation of specific microalgal species, when an accurate model is not available, thus promoting the overall progress and viability of microalgae biomass production. © 2023 Society of Chemical Industry.

Complete recycling of Printed Circuit Boards: From base metal bioleaching in a semi-continuous reactor with dual regulation to gold biosorption with brewer's yeast

Bioleaching is a promising technology for recovering base metals and concentrating precious elements in secondary resources. In this study, the extraction of base metals from printed circuit boards (PCBs) was optimized using a dual control system including biomass encapsulation. Gold recovery was achieved in a second step by chemical dissolution followed by biosorption on brewer's yeast. A dual regulation system, controlling oxidoreduction potential (ORP) and copper concentration, was established. This system comprised two reactors interconnected in a loop: a leaching reactor and a biological reactor. Acidophilic bacteria in the biological reactor recycled ferrous into ferric iron in a low shear stress environment. The Fe3+ solution from the biological reactor recirculated to the leaching compartment (hosting the PCBs) based on ORP regulation (optimal setpoint: +410 mV vs Ag/AgCl). A second control aimed to maintain the copper concentration below the inhibitory level (15 g Cu2+ L−1) by addition of fresh medium and overflow of leachate. This configuration ensured the highest pulp density, operating consistently, in steady state, at the inhibitory concentration limit, the PCBs being added batch-wise when the stock within the leaching compartment was exhausted. Compared to planktonic cells, the encapsulated mode showed optimal base metals leaching yields: 100% for Cu, 24% for Al, 49% for Ni, and 100% for Zn, with a hydraulic retention time (HRT) of 92 h and an iron oxidation rate of 0.455 mg L−1 h−1. The reactor's productivity was 5.91 g Cu L−1d−1, corresponding to the leaching of 20.88 g PCBs L−1d−1. The gold enriched PCBs (from 290 to 450 mg Au kg−1) were then extracted using Aqua regia, with a 24:1 liquid/solid ratio at 80 °C for 2 h. Subsequent biosorption on brewer's yeast (10 g yeast L−1) selectively recovered gold, with 98% efficiency. Biosorption yields for “fresh” yeast resulting from a model brewing (90.8%) and commercial “active dry” yeast (88.7%) were comparable, indicating the potential use of spent yeast from brewing.

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. This paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

Servo and Regulatory Response of an Industrial Fluid Catalytic Cracking (FCC) Unit under Fuzzy Logic Supervisory Control

A self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented in this paper. The controller has been applied to a control study of Fluid Catalytic Cracking unit (FCCU) riser temperature, and regenerator temperature respectively. Comparison between the performance of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID (12.6%) / Fuzzy-PID (6%)); settling time (PID (80 seconds) / Fuzzy-PID (20seconds)); Integral absolute error (PID (14.29) / Fuzzy-PID (8.63)); integral square error (PID(6.713). Fuzzy-PID (4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)) . Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Demonstrating a smart controller in a hospital integrated energy system

Integrated energy systems have recently gained primary importance in clean energy transition. The combination of the electricity, heating and gas sectors can improve the overall system efficiency and integration of renewables by exploiting the synergies among the energy vectors. In particular, real-time optimization tools based on Model Predictive Control (MPC) can considerably improve the performance of systems with several conversion units and distribution networks by automatically coordinating all interacting technologies. Despite the relevance of several simulation studies on the topic, however, it is significantly harder to have an experimental demonstration of this improvement. This work presents a methodology for the real-world implementation of a novel smart control strategy for integrated energy systems, based on two coordinated MPC levels, which optimize the operation of all conversion units and all energy vectors in the short- and long-term, respectively, to account also for economic incentives on critical units. The strategy that was previously developed and evaluated in a simulation environment has now been implemented, as a supervisory controller, in the integrated energy system of a hospital in Italy. The optimal control logic is easily actuated by dynamically communicating the optimal set-points to the existing Building Management System, without having to alter the system configuration. Field data collected over a two-year period, firstly when it was business as usual and when the new operation was introduced, show that the MPC increased the economic margin and revenues from yearly incentives and lowered the amount of electricity purchased, reducing dependency on the power grid.

Distributed cooperative fault tolerant optimal active power control in AC microgrid

In this paper, a new distributed and cooperative fault tolerant control is proposed for the double-function optimal active power control (APC) of distributed generators (DG) in an islanded AC microgrid. Double-function optimal APC consists of the economic dispatch which calculates the optimal active power set-points and coordinates control modules to receive and track the set-points. In the proposed control method, if there is a faulty DG whose capability to satisfy its optimum active power set-point is restricted due to a totally or partially DG failure, the task of reaching the global control goal is re-distributed among the fault-free DGs through designing dynamic feedback set-points. In addition, no central coordinator is considered for the MG, and the new set-points are calculated locally by the fault-free DGs through message exchange over the communication network. Redistribution is performed without requiring any coordination unit and only with the cooperation of DGs on redistributed set-points through local exchange information. The capability of the proposed method is evaluated by considering a microgrid with multiple DGs and analyzing its operational performance under a variety of fault scenarios, as well as providing a qualitative and quantitative comparison with existing literature to demonstrate its efficacy.

Model-based closed-loop control strategies for flex-fuel capability

Meeting future limits for greenhouse gas (GHG) and pollutant emissions represents major challenges for the further development of internal combustion engines for light and heavy-duty applications. To comply with increasingly higher standards, the use of renewable fuels is an important option due to their great potential in terms of energy density, sustainability and low-pollution combustion. However, the increased fuel diversity associated with their use increases the complexity of the powertrain development and calibration process, resulting in an increase in development time and cost. To address these challenges, advanced model-based closed-loop control strategies are applied to optimize the fuel- and air-path settings and make best use of the properties of the different fuels. One such approach is Combustion Rate Shaping (CRS). This ensures a closed-loop control of combustion parameters such as the indicated mean effective pressure, the center of heat release, and the combustion noise excitation. In addition, a model-based closed-loop NOx control could simplify the control on the air-path side. Such a closed-loop approach can further be combined with an onboard fuel detection algorithm and learning functionalities that eventually allow switching to optimal setpoints based on the fuel properties. This publication presents an evaluation of CRS as control concept with flex-fuel operation. The control strategy is implemented on a demonstrator vehicle built up with a Rapid Control Prototyping (RCP) system. The results of emission test cycles with conventional diesel fuel and later with a blend with 1- Octanol, OME3–5 and a paraffinic renewable diesel fuel are presented to illustrate the potential of the novel control concept. The developed CRS concept shows a reduced combustion parameters and emissions dispersion. In particular, the results with OME3–5 blend shows up to 40% reduction in NOx emissions and up to 1 dB(A) lower average combustion noise in comparison to conventional open-loop control. Moreover, an onboard fuel detection and an adaptive control functionality are proposed and experimental validations are carried out to demonstrate the potential to optimize engine emissions.

Dynamic multi-objective optimization and multi-units linear active disturbance rejection control for wastewater treatment processes

Wastewater treatment processes (WWTPs) are complex nonlinear systems with strong nonlinearity among many treatment units and disturbances including weather conditions, uncertainties, and time-varying dynamics. This paper studies the multi-objective and multi-units linear active disturbance rejection control for WWTPs to lower the overall cost index and enhance the effluent quality index simultaneously. A multi-objective optimization method using the multi-strategy mutation and the adaptive mechanism is employed to dynamically optimize set points. Then, multiple-units linear active disturbance rejection controllers are designed to achieve tracking control for dissolved oxygen concentration (including 3-5 units) and nitrate level with unmeasurable disturbances based on the optimal set points. The comparison experiments tested on the benchmark simulation model No. 1 demonstrate that the proposed method can improve the tracking performance and achieve a significant improvement in reducing the overall cost index and the effluent quality index.