Process Disturbances Research Articles

Protocol-based set-membership state estimation for linear repetitive processes with uniform quantization: a zonotope-based approach

This paper is concerned with the zonotopic state estimation problem for a class of linear repetitive processes (LRPs) with weighted try-once-discard protocols (WTODPs) subject to uniform quantization. In such a system, the process disturbance and measurement noise are generally assumed to be unknown but bounded in certain zonotopes. The measurement data are uniformly quantized prior to entering the network. In order to effectively curb data collision, a WTODP is considered, based on which only the selected sensor is allowed to transmit the data through network. The aim of this paper is to find a zonotope that covers all possible states consistent with the system model and WTODP-based measured outputs. By using the zonotope properties, a zonotope containing all possible states is first constructed whose size is then minimized by designing an appropriate correlation matrix. Moreover, a sufficient condition is offered for the existence of an upper bound on the size of this zonotope. At last, we valid the efficacy of the developed estimation approach via an illustrate example.

Observer design for discrete T-S fuzzy time-delay singular systems

In this paper, we investigate the design of observers tailored for discrete T-S fuzzy time-delay singular systems, considering the presence of process disturbances and measurable premise variables. An observer with more design degrees of freedom than the commonly used Luenberger observer is designed. By constructing an asymmetric Lyapunov-Krasovskii functional, a criterion ensuring the existence of the fuzzy state observer is provided, which eliminates the limitations of the positive definiteness and symmetry of the previous functional matrix. In the process of solving the inequality, the relaxation matrix is incorporated to acquire additional flexibility and alleviate the conservatism of parameter design. Ultimately, two numerical instances are provided to thoroughly validate the rationale and efficacy of the approach.

The impact of resistivity on the variability of black hole accretion flows

The accretion of magnetized plasma onto black holes is a complex and dynamic process in which the magnetic field plays a crucial role. The amount of magnetic flux that is accumulated near the event horizon significantly impacts the accretion flow behavior. Resistivity, which is a measure of how easily magnetic fields can dissipate, is thought to be a key factor influencing this process. This work explores the influence of resistivity on the accretion flow variability. We investigated simulations that reached the limit of the magnetically arrested disk (MAD) and simulations with an initial multi-loop magnetic field configuration. We employed 3D resistive general relativistic magnetohydrodynamic (MHD) simulations to model the accretion process under various regimes, where resistivity is globally constant (uniform resistivity). Our findings reveal distinct flow behaviors depending on resistivity. High-resistivity simulations never achieved the MAD state, which indicates a disturbed magnetic-flux accumulation process. Conversely, low-resistivity simulations converged toward the ideal MHD limit. The key results are that i) for the standard MAD model, resistivity plays a minimum role in flow variability, suggesting that flux eruption events dominate the dynamics. ii) High-resistivity simulations exhibit strong magnetic field diffusion into the disk that rearranges the efficient magnetic flux accumulation from the accretion flow. iii) In multi-loop simulations, resistivity significantly reduces the flow variability, which was not expected. However, magnetic flux accumulation becomes more variable as a result of frequent reconnection events at very low resistivity values. This study shows that resistivity affects how much the flow is distorted as a result of the magnetic field dissipation. Our findings provide new insights into the interplay between magnetic field accumulation, resistivity, variability, and the dynamics of black hole accretion.

Implicit Statistical Learning of Brazilian Children With Developmental Dyslexia.

Implicit statistical learning (ISL) is non-conscious learning where participants identify patterns in their environment after repeated exposures. This study verified whether Brazilian children with DD present disturbances in the ISL mechanism and if these disturbances may be related to the difficulties associated with DD through artificial grammar learning (AGL) and reaction time in serial tasks (SRT Task). It also intended to verify which of the paradigms proves to be the most sensitive to assess ISL and which is most associated with participants' learning to read and write. Two groups of children with and without DD from the end of the first cycle and the beginning of the second cycle of the elementary school participated in this study, paired according to socioeconomic level, education network, schooling, gender and age. Children with DD showed no disturbances in the ISL process; the AGL paradigm exhibited the most significant association with performance on reading/writing tasks. When compared to the SRT Task, the AGL paradigm proved to be more sensitive in assessing implicit processes and effectively distinguishing between the groups with and without DD. The results of the SRT Task emphasise the importance of task practice and structure for implicit learning in children with developmental dyslexia. These findings have important implications for understanding ISL and its relevance to reading and writing skills in children with developmental dyslexia.

Balancing Blood Glucose Level by Using Effective Nutrition Strategies and Change of the Lifestyle

The elevation of glucose levels in the blood adversely affects the quality of life. It is a health condition that results in a disturbance or dysfunction of a specific physiological process in the body and may manifest through various symptoms. The purpose of this research is to examine the impact of lifestyle and dietary habits in patients with hyperglycemia and to demonstrate that modifications to these factors can lead to significant improvements and regulation of hyperglycemia levels. To conduct the research, a survey questionnaire was administered to patients in Kosovo who are experiencing issues with elevated blood glucose levels. For this purpose, a total of 200 respondents aged between 27 and 85 were included. The questionnaire comprises 48 survey questions. A statistical analysis of the collected data was conducted using SPSS, Reviews, and MS Excel software applications. The data obtained indicate that the majority of respondents are aged between 51 and 60 years. According to the educational structure, a maximum of 60% has completed secondary education. A total of 23 patients exhibits normal body mass, defined as a Body Mass Index (BMI) ranging from 18.5 to 24.9. In contrast, 38% of patients, equating to 76 individuals, demonstrate excess body mass as determined by BMI criteria. Fifty percent (50%) of patients exhibiting hyperglycemia frequently omit breakfast. Eighty percent (80%) of respondents reported that they do not engage in regular exercise or that their exercise frequency is infrequent. Forty-six (46%) percent of respondents indicated that they occasionally consume fruit in quantities ranging from 250 to 500 grams. More than half of the respondents indicated that they predominantly consume bread and pasta made with whole meal flour, whereas 28% reported not consuming bread and pasta at all. Additionally, 46% of patients occasionally utilize artificial sweeteners, while 57% reported occasional use of natural sweeteners. In conclusion, it can be asserted that enhancing the lifestyle and dietary practices of patients can effectively prevent the progression of the condition and the advancement to type 2 diabetes.

Adaptive neural network based fixed-time attitude tracking control of spacecraft considering input saturation

Aiming at the issues of actuator saturation, inertia uncertainties, and external unknown disturbances in the attitude tracking control process of spacecraft, an adaptive fixed-time attitude control method is proposed, which is based on a radial basis function neural network (RBFNN). Firstly, a spacecraft attitude kinematics and dynamics model is established based on the quaternion method and a Gaussian error function is introduced to constrain the controller amplitude. Secondly, the external unknown disturbances are addressed by a fixed-time disturbance observer, and the controller is designed utilizing the backstepping method. To eliminate the adverse effects caused by actuator saturation, we design an enhanced auxiliary system to improve the stability of the system. Aiming at inertia uncertainties, RBFNN is used to approximate it, and an innovative fixed-time convergence adaptive law with RBFNN weights is devised. Subsequently, based on Lyapunov theory, the fixed time stability of the closed loop system is proven, and an expression for the settling time is given. Finally, simulation analysis validates the effectiveness of the designed controller.

Antithyroglobulin and Antiperoxidase Antibodies Can Negatively Influence Pregnancy Outcomes by Disturbing the Placentation Process and Triggering an Imbalance in Placental Angiogenic Factors.

Background/Objectives: Thyroid autoimmunity (TAI) affects about 15% of women of reproductive age and can negatively affect pregnancy outcomes. One possible mechanism for pregnancy complications can be attributed to a disturbed process of placentation caused by thyroid antibodies. To test this hypothesis, placental hormones and angiogenic factors in pregnant women with TAI were evaluated. Methods: Fifty-eight hypothyroid women positive for TPOAb/TgAb, thirty-three hypothyroid women negative for TPOAb/TgAb, and thirty-nine healthy controls were enrolled in this study. Maternal thyroid function tests were established every month throughout pregnancy, and angiogenic placental factors, pro-angiogenic placental growth factor (PlGF); two anti-angiogenic factors, soluble vascular endothelial growth factor receptor 1 (sFlt-1) and soluble endoglin (sEng); and placental hormones, estradiol, progesterone, and hCG, were determined during each trimester. Results: Obstetrical and neonatal outcomes did not differ between the groups. However, several detrimental effects of thyroid antibodies were observed. These included a positive correlation between TgAb and the sEng/PlGF ratio in the first trimester and positive correlations between TPOAb and sFlt-1 and between TgAb and the sFlt-1/PlGF ratio in the third trimester. TgAbs in the first trimester was a risk factor for gestational hypertension and preeclampsia. Conclusions: Our study indicates that TPOAbs and TgAbs can exert a direct harmful effect on placentation, leading to disturbances in the production of placental angiogenic factors and, consequently, to an increased risk of gestational hypertension and preeclampsia.

Study on the Microscopic Pore Characteristics and Mechanisms of Disturbance Damage in Zhanjiang Formation Structural Clay

To investigate the microscopic pore evolution characteristics of Zhanjiang Formation structural clay during the disturbance process, unconfined compressive strength tests, scanning electron microscopy (SEM), and X-ray diffraction (XRD) were conducted on disturbed samples subjected to various disturbance conditions after vibrational disturbance. Based on the evolution characteristics of the microstructure, the microscopic pore characteristics of the disturbance damage of Zhanjiang Formation structural clay were examined. The results indicate the following. (1) The porosity in three-dimensional visualization images of the microstructure reconstructed by ArcGIS 10.1 increases with the disturbance degree, showing a linear growth trend. (2) The correlation analysis between macroscopic mechanics and microscopic pores shows that the unconfined compressive strength of Zhanjiang Formation structural clay is mainly affected by its porosity, with a significant linear negative correlation. Based on this, a reasonable regression model between the microscopic porosity and the unconfined compressive strength has been established. The model can rapidly estimate the unconfined compressive strength from porosity data, facilitating the assessment engineering properties of the soil. (3) The microscopic pore structure of Zhanjiang Formation structural clay exhibits prominent Menger fractal characteristics. The three-dimensional pore fractal dimension has a certain positive correlation with the disturbance degree, and can be utilized to characterize the pore structure and complexity, serving as a significant parameter for the quantitative evaluation of the pore structure characteristics of Zhanjiang Formation structural clay. Consequently, the complexity of the pore structure of the engineering soil can be evaluated by the pore fractal dimension. (4) The impact of disturbance on soil is primarily manifested in the structural changes in secondary clay minerals, transitioning from a relatively intact to a fully adjusted state. During this process, interparticle pores continuously increase, pore structure complexity increases, and interparticle cementation weakens, resulting in the continuous degradation of unconfined compressive strength. This study contributes to a deeper understanding of the disturbance damage characteristics of Zhanjiang Formation structured clays from a microscopic pore perspective, providing a theoretical basis for the engineering construction and operational maintenance in regions with Zhanjiang Formation structural clay.

Pre-seismic anomalies and co-seismic disturbance induced by the 2024 Mw 7.1 Wushi earthquake: multi-GNSS observations and modeling

This study investigates ionospheric anomalies associated with the Mw 7.1 Wushi earthquake that occurred in Xinjiang, China, on January 22, 2024. We analyze multi-GNSS data, including GPS and BeiDou (BDS), from the Crustal Movement Observation Network of China (CMONOC) to detect pre-seismic ionospheric anomalies and co-seismic ionospheric disturbances (CIDs). A 27-day sliding interquartile range method is applied to BDS geostationary satellite data to identify pre-seismic anomalies while minimizing the influence of solar and geomagnetic activity. The results show that BDS geostationary satellites significantly improve the spatiotemporal resolution of ionospheric monitoring, and M_GIM outperforms CODE-TEC in regions with sparse IGS stations. Pre-seismic anomalies are observed on January 14, 15, 16, and 18, with those southeast of the epicenter possibly caused by additional electric fields generated in the seismic preparation zone. CIDs are detected within 400 km of the epicenter using the Butterworth filtering technique, revealing a propagation speed of approximately 1 km/s, likely caused by acoustic-gravity waves generated by the earthquake. This study demonstrates the importance of multi-GNSS observations and high-resolution regional modeling for detecting and understanding seismo-ionospheric anomalies, providing insights into the earthquake preparation process and ionospheric disturbance propagation characteristics.

On-line recognition of mixture control chart patterns using hybrid CNN and LSTM for auto-correlated processes

Statistical process control (SPC), designed to detect and identify process disturbances, is an effective quality control method for ensuring process stability. Owing to the growing automation of the industry, the manufacturing process can be autocorrelated. Engineer process control (EPC) is typically used to address autocorrelation. However, process disturbances can be offset by feedback compensation, making control chart patterns (CCPs) difficult to identify. Most studies on control chart pattern recognition (CCPR) techniques are based on a single abnormal control chart pattern (CCP). However, a mixture of CCPs can occur during real-world manufacturing processes. With the development of intelligent manufacturing systems, early detection of abnormal concurrent CCPs has become an important issue. In this study, a hybrid model combining a convolutional neural network (CNN) and long short-term memory (LSTM) in an online detection system was used to recognize the concurrent CCPR problem in SPC-EPC processes. The results showed that the average accuracy of the deep learning CNN-LSTM method was 99.83%, which was significantly better than that of the machine learning method. In addition, the running time of the CNN-LSTM model was shortened. In a comparison of online monitoring between the deep learning method and the machine learning method, the CNN-LSTM model for an online monitoring system identified abnormal concurrent patterns faster. Therefore, the proposed CNN-LSTM online monitoring scheme can be applied confidently and successfully to identify mixture CCPs in an SPC-EPC process.

A frequency domain fractional order tilted integral derivative controller design for fractional order time delay processes

AbstractIn real‐time processes, time delays are inherent due to various factors, such as delays in volume, mass, and information transfer, leading to a deterioration in process performance and stability. Therefore, this paper proposes a fractional‐order tilted integral derivative (TID) controller design for stable and unstable fractional‐order processes with time delay, where the optimal controller parameters are designed using a deterministic approach. An inner loop controller is designed using stability analysis and a graphical approach for unstable fractional‐order time delay processes. The proposed approach offers flexibility and fine‐tuning in designing the controller for a specific application. A systematic reference to the disturbance ratio is carried out to assess the disturbance handling capacity of the proposed controller. It shows the sensitivity of the designed controller against disturbances in the frequency spectrum graphically. Numerous performance indices and time‐domain parameters are computed and compared with state‐of‐the‐art techniques to analyze the efficacy. Furthermore, it is validated on the experimental setup of a four‐tank system. Simulation and experimental results demonstrate that the proposed approach outperforms recent techniques in terms of process control and disturbance rejection.

Fractal fracture characteristics and acoustic emission analysis of soft rock‐coal combinations under low‐frequency disturbance

AbstractTo explore the fracture mode and failure characteristics of soft rock‐coal combinations under different disturbance amplitudes, this paper conducted dynamic uniaxial compression tests under different disturbance amplitudes. The results indicate that low‐frequency disturbances can weaken the load‐bearing capacity of the specimen. The failure modes of specimens vary under different disturbance amplitudes, and low amplitude disturbances are not sufficient to cause overall cracking and failure of the specimens. In addition, as the disturbance amplitude increases, the fractal dimension of the composite after fracture shows a fluctuating trend, reaching its maximum value at 3 MPa and its minimum value at 4 MPa, with an overall range of 1–3. Finally, the absolute energy and waveform characteristics of acoustic emission can display the fracture damage of the specimen during the disturbance process. This study provides a theoretical basis for understanding and preventing coal rock dynamic disasters.

Ecological Restoration Increases the Diversity of Understory Vegetation in Secondary Forests: An Evidence from 90 Years of Forest Closures

Ecological restoration and biodiversity are currently hot issues of global environmental concern. However, knowledge about the specific impacts of restoration duration on understory vegetation diversity remains limited. Therefore, this study comprehensive employed a spatial approach to compare the differences in understory plant diversity and species composition among secondary forests with varying ecological restoration ages (0, 10, 30, 60, and 90-year-old stands) in Huangfu Mountain National Forest Park. This methodology allowed us to clarify the key factors affecting the composition of the understory plant community and investigate the regulatory mechanisms influencing changes in understory plant diversity. The results showed that shrub Shannon’s index value, shrub evenness, herb Shannon’s index value, herb richness, and herb evenness were significantly affected by the years of restoration, with 10 years and 90 years being the highest and 60 years being the lowest. Substrate diversity was the main factor influencing plant diversity in the shrub layer. Overstory richness, soil C/N, soil C, soil N, soil bacterial Observed OTUs, soil bacterial Chao1, soil bacterial Pielou_e, and substrate diversity were the drivers of plant diversity in the herb layer. Overstory evenness had a direct effect (0.256) and an indirect effect (0.284) on herb evenness through light availability and fungal Simpson’s index value. Light availability directly negatively affected herb evenness (−0.360). In addition, 52.6% of the factors affecting the herb evenness index were from the arboreal layer evenness, light availability, and fungal Simpson’s index value. To sum up, moderate disturbance of the understory environment of natural secondary forests can be carried out after 10 years of restoration, which is more conducive to the increase of understory plant diversity. This comprehensive study provides a theoretical basis for formulating ecological restoration measures for secondary forests, particularly in understanding the optimal timing and nature of disturbance in the restoration process, reassuring the audience about the validity and reliability of the findings.

Enhancing sustainability in palm oil industry: reinforcement learning for renewable energy management considered climatic variability

Energy sources are critical in the industrial sector, particularly as population growth intensifies the pressure on industries to scale up production to meet increasing demands. Integrating renewable energy sources such as biomass, biogas, and photovoltaic systems in the palm oil production process can be considered a pivotal strategy for mitigating carbon emissions. However, load fluctuations due to mill size and insufficient supply of biogas and photovoltaic are significant challenges in energy management for the palm oil industry. Adopting renewable energy solutions is hindered by process disturbances and climatic variability caused by weather, ambient temperature, relative humidity, and solar radiation. These factors pose constraints on the implementation of renewable energy solutions. Therefore, this study aims to fill the gap in terms of renewable energy management within the palm oil industry by applying reinforcement learning to achieve effective energy management and reduce carbon emissions under five climatic scenarios. The result shows that reinforcement learning effectively combines renewable energy sources to generate electricity and steam while efficiently managing energy storage without exceeding predefined levels. Additionally, deploying a photovoltaic system contributes to energy savings from biomass and biogas of 2.9% and reduces average daily carbon dioxide emissions by 3137 kg. The proposed method has proved beneficial across all climate scenarios for energy savings and emission reduction.

Simultaneous interval estimation of actuator fault and state for a class of nonlinear systems by zonotope analysis

In this paper, an actuator fault and state interval estimation method for a class of nonlinear systems is proposed by integrating observer design and zonotope analysis. For the considered systems, we present a novel unknown input observer structure with broad applications. The design procedure is based on H∞ method to decrease the influence of unknown but bounded process disturbances and measurement noise. Moreover, a novel interval estimation method is presented based on zonotope analysis to obtain tighter intervals. Numerical simulations of a quadruple-tank system are conducted to assess the performance of the proposed approach.

Hyperspectral Imaging Technique to Characterize Digestate and Visualize Physical Impurities in Anaerobically Digested Biowaste.

Methods used to monitor anaerobic digestion (AD) indicators are commonly based on wet chemical analyses, which consume time and materials. In addition, physical disturbances, such as floating granules (FGs), must be monitored manually. In this study, we present an eco-friendly, high-throughput methodology that uses near-infrared hyperspectral imaging (NIR-HSI) to build a machine-learning model for characterizing the chemical composition of the digestate and a target detection algorithm for identifying FGs. A total of 732 digestate samples were used to develop and validate a model for calculating total nitrogen (TN), total organic carbon (TOC), total ammonia nitrogen (TAN), and chemical oxygen demand (COD), which are the chemical indicators of responses to disturbances in the AD process. Among these parameters, good model performance was obtained using the dried digestates data set, where the coefficient of determination (R2test) and the root-mean-square error (RMSEtest) were 0.82 and 1090 mg/L for TOC, and 0.86 and 690 mg/L for TN, respectively. Furthermore, the unique spectral features of the FGs in reactors with a lipid-rich substrate meant that they could also be identified by the HSI system. Based on these findings, developing NIR-HSI solutions to monitor the digestate properties in AD plants has great potential for industrial application.

Adaptive complementary sliding mode control of ship course under environmental disturbance

In this paper, an adaptive complementary sliding mode control strategy based on nonlinear extended state observer (NESO) is proposed for the ship course tracking control subject to environmental disturbances. By introducing the adaptive law, the system uncertainty is estimated and adjusted, so that the boundary layer of the complementary sliding mode control can realize dynamic changes and ensure the system robustness. At the same time, the auxiliary control system is introduced to compensate the complementary sliding mode control law to guarantee the system stability under finite actuation. For the environmental disturbances of the heading process, a NESO is designed for real-time estimation and compensated to the adaptive complementary sliding mode control law to improve the robustness of the system. The closed-loop stability is proved, and simulation results show that the control strategy effectively improves the tracking accuracy and robustness of the system.

Computational Analysis of Boerhavia diffusa Plant Extracts Targeting Alpha actinin4 against Focal Segmental Glomerulosclerosis

The most common protein lost in urine (proteinuria) is albumin, which is a symptom of the kidney illness focal segmental glomerulosclerosis (FSGS). It causes damage to podocyte foot processes, resulting in effacement of foot processes, and injury to foot processes leads to the leaking of plasma proteins into the urine, resulting in nephrotic syndrome. Alpha actinin (ACTN4) is extensively produced in glomerular podocytes, has a function in non-muscle cytoskeletal activity, and is enhanced early in the course of nephrotic illness in various types. Mutations in ACTN4 induce autosomal and sporadic steroid-resistance nephritic syndrome, which causes disturbance in podocyte foot process and function. All of the FSGS-causing mutations are situated on actin actin-binding domain of the ACTN4 protein. FSGS is typically treated by decreasing dietary salt and using immune-suppressing medications such as glucocorticoids. There is a risk associated with these drugs for cancer patients. Several studies have found that up to 80% of individuals with primary FSGS are resistant to steroid therapy, even though all other therapeutic options have been exhausted. Patients with steroid-resistant FSGS have a higher incidence of end-stage renal failure. Therefore, several new treatment strategies were put forward to cure the disease form of which use of phytochemicals is one such sustainable modality. In addition to this, our study intended to predict the stability of mutant protein’s structure as compared to wild type structure through molecular dynamic simulation analysis. With this, we extend our study to predict the potency of phytochemicals of Boerhaviadiffusa against FSGS by using molecular docking analysis. From our study, we conclude that the actin-binding domain of ACTN4 protein becomes more unstable or loses its stability after the mutation at position W59R among all the studied mutation positions. The boeravinone F phytochemical of the Boerhaviadiffusa plant shows the best inhibitory effect on the mutant actin-binding domain of ACTN4 protein and it confirms a stable binding confirmation at minimum energy of -7.5 kcal/mol. Hence it may be taken into consideration for future research work.

Physiological Auricular and Cornual Asymmetries of the Sanmartinero Creole Bovine.

Asymmetric studies can indicate disturbances in the developmental process. Fluctuating asymmetry (FA) is considered an indicator of stress. The Sanmartinero (SM) creole bovine is native to the department of Meta (Colombian Orinoquia) and its adaptation process has allowed it to live in extreme tropical environments. The aim of this cross-sectional and descriptive study was to present the current state of the knowledge of asymmetries in some cephalic characters of the SM creole bovine. A total of 94 animals were studied (18 uncastrated males and 76 females) from three different farms, with an age range of 0.5-10 years. For each animal, two measurements of the ear (width and length) and two measurements of the horn (perimeter and length) were obtained in vivo. The degree of asymmetry was calculated as (R - L)/(R + L). Bilateral differences pointed towards a fluctuating asymmetry (e.g., a random variation in the trait that is expected to be perfectly symmetrical) biased towards right for ear width and horn perimeter, and towards left for ear and horn length. Since the development of these structures-ears and horns-is under the control of the same set of genes, the fluctuating asymmetry could constitute a reflection of a normal condition.

Energy-efficient semi-continuous distillation of a ternary mixture using combined tracking-economic model predictive control

Distillation is an energy-intensive separation technique. Semicontinuous distillation has gained attention as a cost-effective alternative to conventional multi-column distillation of multi-component mixtures. However, the cyclic behavior of semicontinuous distillation poses operational challenges considering cost of energy and need for maintaining consistent product purity. The challenge is addressed in this work by proposing a dual-objective Model Predictive Controller (MPC) that handles both product purity and energy consumption through a combined tracking-economic objective function. The proposed MPC features Extended Kalman Filter for state estimation and the successive linearization technique for building the prediction model. The nonlinear plant model is implemented in OpenModelica, which is linked to Matlab for MPC implementation. The effectiveness of the proposed MPC is demonstrated on semicontinuous distillation of Benzene, Toluene, and o-Xylene. The dual-objective MPC is shown to yield an energy saving of 8% per feed processed compared with conventional tracking MPC, while also performing well under process disturbances. It is also shown that the feed processed during a certain time period is 8% higher in the dual-objective MPC than the tracking MPC. The considerable economic improvement is gained without degrading the product purities, indicating that dual-objective MPC is an effective approach to energy-efficient operation of semicontinuous distillation.