Search Results (15)

Digital Twins (DTs) are increasingly deployed in Industry 4.0 to enable real-time monitoring, analysis, and control, yet the transition from isolated DT instances to plant-wide ecosystems across cloud and edge infrastructures introduces fragmentation and coordination challenges among heterogeneous assets, data sources, and services. This paper addresses this gap by proposing a cloud-native Digital Twin Computing Layer (DTCL) that provides a unified control and orchestration plane for composing and operating DT applications in Smart Manufacturing. The DTCL is designed as a three-tier architecture comprising a developer-facing user interface, a Deploy Engine for automated deployment and lifecycle management, and a Service Catalog of reusable, independently deployable microservices. Standardized interaction is supported through semantic DT models and API- and message-based communication mechanisms. A governance workflow, based on service discovery and validation, is introduced to support non-redundant integration and controlled evolution of services. The approach is demonstrated through a Smart Manufacturing predictive maintenance case study and further extended with a Smart Mobility scenario for urban public transport planning, highlighting the flexibility of the DTCL across different application domains. Overall, the DTCL supports modular composition, interoperability, and lifecycle governance across heterogeneous Digital Twin applications, providing a scalable foundation for both industrial and urban data-driven scenarios. Full article

Microsieving-based advanced primary treatment (APT) has attracted increasing attention as an approach for restructuring carbon and energy flows within wastewater treatment plants (WWTPs). Unlike previous work that has often addressed individual microsieving technologies or specific recovery routes separately, this review provides a unified framework for comparing drum screens (DSs)/drum filters (DFs), cloth disc filters (CDFs), and rotating belt filters (RBFs) with conventional primary sedimentation (PST) in terms of separation mechanisms and pollutant capture. On this basis, it further discusses recent progress in energy and resource recovery from primary screenings, together with their relevance to energy demand reduction and carbon redistribution in WWTPs. Current limitations arise at two levels. Microsieving technologies remain constrained by mesh fouling and limited control over selective pollutant capture, while plant-wide evidence remains insufficient, particularly regarding techno-economic assessment of recovered products and life cycle assessment of full plant performance after replacing primary sedimentation. Future work should therefore focus on targeted process optimization and plant-wide evaluation of economic and environmental feasibility. Full article

Rapeseed (Brassica napus L.) faces escalating threats from abiotic and biotic stresses, notably blackleg caused by Leptosphaeria maculans. Due to limited chemical control efficacy and stringent GMO regulations, marker-assisted selection (MAS) leveraging natural genetic variation has become an indispensable strategy for crop improvement. This study identified novel molecular markers for blackleg resistance by integrating genome-wide association study (GWAS) results with high-throughput genotyping by Diversity Arrays Technology sequencing. Phenotypic screening across the population demonstrated a wide spectrum of disease severity (scores 0–6), confirming the segregation of key resistance genes. The DArTseq platform identified nearly 104,000 markers, comprising 61% SilicoDArTs and 39% SNPs. Among the 33 most significant markers associated with resistance (p < 0.01), 76% were SilicoDArTs. Transcriptomic data further validated these findings, revealing 13 marker-linked genes expressed during infection, seven of which exhibited significant differential expression. Comprehensive functional annotation of Arabidopsis thaliana orthologs associated these genes with diverse cellular and plant-wide processes, particularly during stress responses. Collectively, these findings emphasize the complex polygenic nature of blackleg resistance and provide robust genomic tools for the accelerated breeding of resilient B. napus cultivars. Full article

Wastewater Treatment Plants (WWTPs) rely on automatic control strategies to regulate pollutant concentrations and comply with environmental standards. Among them, Proportional Integral (PI) controllers are widely adopted for their simplicity and robustness, yet their effectiveness is limited by the nonlinear and time-varying dynamics of biological processes. In this work, Long Short-Term Memory (LSTM)-based Artificial Neural Network (ANN) PI controllers are proposed as data-driven replacements for conventional PIs in key WWTP feedback loops. Using the Benchmark Simulation Model No. 1 (BSM1), ANN controllers were trained to replicate the behavior of default nitrate and nitrite nitrogen ($S_{NO,2}$) and dissolved oxygen ($S_{O,5}$) loops, under both time-agnostic and time-aware strategies with three- and four-input configurations. The four-input time-aware model delivered the best results, reproducing PI behavior with high accuracy (coefficient of determination, $R^2\approx 0.99$) and considerably reducing control errors. For instance, under storm influent conditions, the $S_{O,5}$ controller reduced the Integral of Squared Error ($\mathrm{ISE}$) and Integral of Absolute Error ($\mathrm{IAE}$) by 84.7% and 68.4%, respectively, compared with the default PI. Beyond loop-level improvements, a Transfer Learning (TL) extension was explored: the trained $S_{O,5}$ controller was directly applied to additional aerated reactors ($S_{O,3}$ and $S_{O,4}$) without retraining, replacing fixed aeration and demonstrating adaptability while reducing design effort. Plant-wide evaluation with the $S_{NO,2}$ loop and three dissolved oxygen loops ($S_{O,3}$–$S_{O,5}$), all controlled by LSTM-based PI controllers, under storm influent conditions, showed further reductions in the Effluent Quality Index ($\mathrm{EQI}$) and the Overall Cost Index ($\mathrm{OCI}$) by 0.84% and 1.47%, respectively, highlighting simultaneous gains in effluent quality and operational economy. Additionally, the actuator and energy analyses showed that the LSTM-based controllers produced realistic and smooth control signals, maintained consistent energy use, and ensured stable overall operation, confirming the practical feasibility of the proposed approach. Full article

Natural gas-to-methanol plants are receiving renewed interest with the significant increase in shale gas availability. Methanol serves as a crucial raw material for producing various industrial and consumer goods as well as key platform chemicals, including acetic acid, methyl tertiary butyl ether, dimethyl ether, and methylamine. In this research, a dynamic model is developed for Natgasoline’s methanol manufacturing plant. A hierarchical control system comprising Dynamic Matrix Control (DMC) and a basic regulatory control loop is constructed using this dynamic model to minimize methanol losses and utility costs under various process upsets. A subspace identification methodology is used to develop rigorous DMCplus controller models. The simulation results in the ASPEN manufacturing software platform show that the DMCplus controller developed in this study can reduce methanol losses by 96% and utility requirements by 40%. The controller is robust to feed flow variations of ±10%. Furthermore, disturbances due to the variation in hydrogen content in the syngas are also successfully rejected by the controller. This hierarchical multivariable control system performs significantly better than the traditional regulatory PID control strategy in optimizing the methanol process under process constraints. Full article

This paper presents a plantwide control strategy for optimizing a pressure-swing azeotropic distillation process used in tetrahydrofuran dehydration. Leveraging Skogestad’s methodology, this strategy focused on two distillation columns: a low-pressure column for water recovery at 20 psia and a high-pressure column that achieved 0.99 molar fraction purity of tetrahydrofuran at 115 psia. This study identified critical control variables through plant analysis by implementing PI controllers in the regulatory control layer to stabilize flows and pressures. In the supervisory control layer, a PI controller combined with MIMO MPC effectively enhanced the product purity and reduced the energy consumption by 36%. Stable inlet and outlet flow conditions (100 lbmol/hr inlet, 29.59 lbmol/hr outlet) were maintained without compromising the equipment integrity. The operational ranges for the process included variations in the tetrahydrofuran mole fraction from 0.25 to 0.35 at the inlet, which demonstrated a robust performance across perturbations. These achievements signify significant advancements in process efficiency and sustainability, offering substantial reductions in energy usage while ensuring consistent high purity levels in tetrahydrofuran production. The developed control structure sets a new standard for efficient azeotropic distillation processes, with implications for enhancing operational performance across industrial applications. Full article

This work addresses the design of an optimized control system for an acrylic acid plant through the lens of the Stochastic Plant-Wide Optimizing Control (S-PWOC) framework. The S-PWOC employs stochastic optimization methods and advanced computer modeling to optimize plant performance by dynamically adjusting operational parameters under varying uncertainties. A comparison between the proposed S-PWOC model and two conventional approaches, the two-level identification method and the typical plant-wide decentralized control structure, highlights the advantages of S-PWOC despite its higher computational demands. Experimental results demonstrate significant improvements, including a 15% increase in process efficiency, a 10% reduction in energy consumption, enhanced product quality consistency, and greater economic viability. Additionally, S-PWOC proves effective in reducing safety risks and improving control efficiency, making it a robust solution for handling uncertainties in real-world plant operations. Full article

Wastewater treatment is an energy-intensive process for treating liquid-phase pollutants in urban settlements. The aerobic processes of the biological treatment involve a significant air demand. An optimal control strategy could be used to minimize the amount of excess air entering the system due to safety factors applied in the design procedures. A plant-wide mechanistic modeling approach including an activated sludge model and one-dimensional settler model was proposed as an effective tool for predicting the actual air demand and for selecting the optimal aeration strategy. In this study, a sewage treatment plant receiving strong influent flow was investigated. At the sludge ages of 14–18 days, the plant was capable of achieving a 90% organic matter reduction and 85% nutrient reduction. By applying a constant dissolved oxygen concentration of 1.5 mg/L, the air demand decreased by 25%, which could be further increased by 10% if the cascade ammonium control approach was applied at peak periods. The dependence of the aeration energy demand on the temperature and dissolved oxygen was formulated, meaning the operators could select the optimal setpoint and minimize the energy consumption while the effluent quality requirements were met. Full article

(1) Background: A proposal for the automatic control of sugar cane honey factories based on simulation with real data is presented. (2) Methods: The P&ID diagram of the artisanal process is designed, as well as the measurement and control systems of the different process variables. A data acquisition and monitoring system is proposed with all the required equipment. Using GNU Octave software, the process was simulated, where the transfer functions and parameters of the different stages were determined. The transient responses of these systems are determined before step-jump type disturbances, as well as that of the controllers. (3) Results: A correct adjustment of the controllers is obtained, indicating those that work in a stable way before disturbance variations in the real ranges of plant work. (4) Conclusions: Simulation of controllers before different forcing functions in the ranges of the operating parameters allowed for establishing dynamic responses of each one, demonstrating that they are capable of adjusting the value of the variable of interest or the control, and determining control of the main operating variables. Full article

Offshore produced water treatment (PWT) accounts for cleaning the largest waste stream in the offshore oil and gas industry. If this separation process is not properly executed, large amounts of oil are often directly discharged into the ocean. This work extends two grey-box models of a three-phase gravity separator and a deoiling hydrocyclone, and combines them into a single plant-wide model for testing PWT control solutions in a typical process configuration. In simulations, three known control solutions—proportional-integral-derivative (PID) control, ${\mathrm{H}}_{\infty }$ control, and model predictive control (MPC)—are compared on the combined model to evaluate the separation performance. The results of the simulations clearly show what performance metrics each controller excels at, such as valve wear, oil discharge, oil-in-water (OiW) concentration variance, and constraint violations. The work incentivizes future control to be based on operational policy, such as defining boundary constraints and weights on oil discharge, rather than maintaining conventional intermediate performance metrics, such as water level in the separation and pressure drop ratio (PDR) over the hydrocyclone. Full article

Simultaneous removal of nitrogen and phosphorous is a recommended practice while treating wastewater. In the present study, control strategies based on proportional-integral (PI), model predictive control (MPC), and fuzzy logic are developed and implemented on a plant-wide wastewater treatment plant. Four combinations of control frameworks are developed in order to reduce the operational cost and improve the effluent quality. As a working platform, a Benchmark simulation model (BSM2-P) is used. A default control framework with PI controllers is used to control nitrate and dissolved oxygen (DO) by manipulating the internal recycle and oxygen mass transfer coefficient (K_La). Hierarchical control topology is proposed in which a lower-level control framework with PI controllers is implemented to DO in the sixth reactor by regulating the K_La of the fifth, sixth, and seventh reactors, and fuzzy and MPC are used at the supervisory level. This supervisory level considers the ammonia in the last aerobic reactor as a feedback signal to alter the DO set-points. PI-fuzzy showed improved effluent quality by 21.1%, total phosphorus removal rate by 33.3% with an increase of operational cost, and a slight increase in the production rates of greenhouse gases. In all the control design frameworks, a trade-off is observed between operational cost and effluent quality. Full article

This work explores the possibilities of improving the eco-efficiency of Wastewater Treatment Plants (WWTPs) introducing a plant-wide perspective in the formulation of the control strategy. Eco-efficiency goals are contemplated in the analysis of the appropriateness of control actions, considering the seasonal effects of temperature into the decision-making process. Plant-wide control strategy handles are the operation variables of the activated sludge process, the volume of the primary clarifier, and the temperature of the anaerobic digester. Performance is evaluated in terms of energy use, biogas production, effluent quality, emissions to air and soil, considering annual and bimestrial average values of indicators to capture seasonal effect of temperature. The result is a set of possible solutions, obtained from a multi-objective decision-making procedure, consisting on a sequence of control actions applied at different temporal windows that improve the eco-efficiency indicators of the plant. The results obtained when applying the different solutions make evident how the application of plant-wide control strategies is useful to improve performance indicators that represent individual goals, leading to trade-off solutions that describe WWTPs’ eco-efficiency. Full article

In this paper, a control approach for improving the overall efficiency of a wastewater treatment plant (WWTP) is presented. It consists of a cascaded control system that uses a global performance indicator as the controlled variable to drive the plant to operating conditions that satisfies trade-offs involved in the WWTP operation, improving the global performance of the plant. The selected global performance indicator is the N/E index that measures the ratio between the amount of nitrogenated compounds eliminated (kgN) and the energy (kWh) required to achieve that goal. This index links the variables of the activated sludge process with the energy consumed in the whole plant, thus the control strategy takes actions based on plantwide considerations. An external Proportional Integral (PI) controller changes the DO set point according to the N/E index and the basic dissolved oxygen (DO) control scheme in the activated sludge process follows this reference changes varying the aeration intensity. An outer loop with an event-based controller is used to compute the index values when the DO concentration is driven to excessively low limits, preventing long operation periods in this undesirable condition. Simple proportional integral controllers (PI) are used to adapt the strategy to the automation systems available in WWTPs. The implementation in the Benchmark Simulation Model 2 (BSM2) demonstrates the potential of the proposed approach. The results show the possibilities of the N/E index to be used as an indicator of global performance of WWTPs. It provides a link between water line objectives and energy consumption in the whole plant that can be exploited to introduce plantwide considerations in alternative control strategies formulated to drive the plant to operating conditions that optimize the overall process efficiency. Full article

Traditional energy production plants are increasingly forced to cycle their load and operate under low-load conditions in response to growth in intermittent renewable generation. A plant-wide dynamic model of a supercritical pulverized coal (SCPC) power plant has been developed in the Aspen Plus Dynamics® (APD) software environment and the impact of advanced control strategies on the transient responses of the key variables to load-following operation and disturbances can be studied. Models of various key unit operations, such as the steam turbine, are developed in Aspen Custom Modeler® (ACM) and integrated in the APD environment. A coordinated control system (CCS) is developed above the regulatory control layer. Three control configurations are evaluated for the control of the main steam; the reheat steam temperature is also controlled. For studying servo control performance of the CCS, the load is decreased from 100% to 40% at a ramp rate of 3% load per min. The impact of a disturbance due to a change in the coal feed composition is also studied. The CCS is found to yield satisfactory performance for both servo control and disturbance rejection. Full article

In modern industrial plants, process units are strongly cross-linked with eachother, and disturbances occurring in one unit potentially become plant-wide. This can leadto a flood of alarms at the supervisory control and data acquisition system, hiding the originalfault causing the disturbance. Hence, one major aim in fault diagnosis is to backtrackthe disturbance propagation path of the disturbance and to localize the root cause of thefault. Since detecting correlation in the data is not sufficient to describe the direction of thepropagation path, cause-effect dependencies among process variables need to be detected.Process variables that show a strong causal impact on other variables in the process comeinto consideration as being the root cause. In this paper, different data-driven methods areproposed, compared and combined that can detect causal relationships in data while solelyrelying on process data. The information of causal dependencies is used for localization ofthe root cause of a fault. All proposed methods consist of a statistical part, which determineswhether the disturbance traveling from one process variable to a second is significant, and aquantitative part, which calculates the causal information the first process variable has aboutthe second. The methods are tested on simulated data from a chemical stirred-tank reactorand on a laboratory plant. Full article