Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces.

In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators' manual conduction and/or previous level 2 systems control.

Advanced Process Control for Clinker Rotary Kiln and Grate Cooler.

The cement industry includes energy-intensive processes, e.g., clinker rotary kilns and clinker grate coolers. Clinker is obtained through chemical and physical reactions in a rotary kiln from raw meal; these reactions also involve combustion processes. The grate cooler is located downstream of the clinker rotary kiln with the purpose of suitably cooling the clinker. The clinker is cooled through the action of multiple cold air fan units as it is transported within the grate cooler. The present work describes a project where Advanced Process Control techniques are applied to a clinker rotary kiln and a clinker grate cooler. Model Predictive Control was selected as the main control strategy. Linear models with delays are obtained through ad hoc plant experiments and suitably included in the controllers' formulation. A cooperation and coordination policy is introduced between the kiln and the cooler controllers. The main objectives of the controllers are to control the rotary kiln and grate cooler critical process variables while minimizing the fuel/coal specific consumption of the kiln and the electric energy consumption of the cold air fan units within the cooler. The overall control system was installed on the real plant, obtaining significant results in terms of service factor and control and energy-saving performances.

Observability Analysis and Observer Design for a Nonlinear Three-Tank System: Theory and Experiments.

This paper addresses the observability analysis and observer design for a nonlinear interacting three-tank system. The plant configuration is first described using the process and instrumentation diagram (P&ID) and a state-space realization is derived; some insights about the behavior of the nonlinear system, considering equilibrium points and the phase portrait are provided. Then, observability in the Hermann-Krener sense is analyzed. A high-gain observer (HGO) is then designed, using the equivalence of the original state-space realization with its observability canonical form, in order to guarantee convergence of the state estimation. The performance was validated through simulation and experiments in a multipurpose plant equipped with real sensors; the HGO response was compared to a Luenberger observer (for a linear approximation of the plant) and the Extended Kalman Filter (for which convergence is not guaranteed), considering nonlinearities, interaction, disturbances and noise. Theoretical and experimental results show that the HGO can provide robust estimation and disturbance rejection, despite the sensitivity of HGOs to noisy variables in processes such as level of liquids.

Recent developments of artificial intelligence in drying of fresh food: A review.

Intellectualization is an important direction of drying development and artificial intelligence (AI) technologies have been widely used to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in different food drying technologies due to the advantages of self-learning ability, adaptive ability, strong fault tolerance and high degree robustness to map the nonlinear structures of arbitrarily complex and dynamic phenomena. This article presents a comprehensive review on intelligent drying technologies and their applications. The paper starts with the introduction of basic theoretical knowledge of ANN, fuzzy logic and expert system. Then, we summarize the AI application of modeling, predicting, and optimization of heat and mass transfer, thermodynamic performance parameters, and quality indicators as well as physiochemical properties of dried products in artificial biomimetic technology (electronic nose, computer vision) and different conventional drying technologies. Furthermore, opportunities and limitations of AI technique in drying are also outlined to provide more ideas for researchers in this area.

Closed loop control of lactate concentration in mammalian cell culture by Raman spectroscopy leads to improved cell density, viability, and biopharmaceutical protein production.

Accumulation of lactate in mammalian cell culture often negatively impacts culture performance, impeding production of therapeutic proteins. Many efforts have been made to limit the accumulation of lactate in cell culture. Here, we describe a closed loop control scheme based on online spectroscopic measurements of glucose and lactate concentrations. A Raman spectroscopy probe was used to monitor a fed-batch mammalian cell culture and predict glucose and lactate concentrations via multivariate calibration using partial least squares regression (PLS). The PLS models had a root mean squared error of prediction (RMSEP) of 0.27 g/L for glucose and 0.20 g/L for lactate. All glucose feeding was controlled by the Raman PLS model predictions. Glucose was automatically fed when lactate levels were beneath a setpoint (either 4.0 or 2.5 g/L) and glucose was below its own setpoint (0.5 g/L). This control scheme was successful in maintaining lactate levels at an arbitrary setpoint throughout the culture, as compared to the eventual accumulate of lactate to 8.0 g/L in the historical process. Automated control of lactate by restricted glucose feeding led to improvements in culture duration, viability, productivity, and robustness. Culture duration was extended from 11 to 13 days, and harvest titer increased 85% over the historical process. Biotechnol. Bioeng. 2016;113: 2416-2424. © 2016 Wiley Periodicals, Inc.

Parameter Identification of the Fractional-Order Mathematical Model for Convective Mass Transfer in a Porous Medium.

Fractional calculus is an essential tool in studying new phenomena in hydromechanics and heat and mass transfer, particularly anomalous hydromechanical advection-dispersion considering the fractal nature of the porous medium. They are valuable in solving the urgent problem of convective mass transfer in a porous medium (e.g., membranes, filters, nozzles, convective coolers, vibrational prillers, and so on). Its solution allows for improving chemical engineering and technology workflows, refining process models for obtaining porous granular materials, realizing the convective cooling of granular and grain materials, and ensuring the corresponding apparatuses' environmental safety. The article aims to develop a reliable convective mass transfer model for a porous medium and proposes a practical approach for its parameter identification. As a result, a general scientific and methodological approach to parameter identification of the fractional convective mass transfer model in a porous medium was proposed based on available experimental data. It mainly used Riemann-Liouville fractional time and coordinate derivatives. The comprehensive application of the Laplace obtained the corresponding general solution transform with respect to time and a coordinate, the Mittag-Leffler function, and specialized functions. Different partial solutions in various application case studies proved this solution. Moreover, the algorithm for practically implementing the developed approach was proposed to evaluate parameters for the considered model by evaluation data. It was reduced to the two-parameter model and justified by the available experimental data.

PAT implementation for advanced process control in solid dosage manufacturing - A practical guide.

The implementation of continuous pharmaceutical manufacturing requires advanced control strategies rather than traditional end product testing or an operation within a small range of controlled parameters. A high level of automation based on process models and hierarchical control concepts is desired. The relevant tools that have been developed and successfully tested in academic and industrial environments in recent years are now ready for utilization on the commercial scale. To date, the focus in Process Analytical Technology (PAT) has mainly been on achieving process understanding and quality control with the ultimate goal of real-time release testing (RTRT). This work describes the workflow for the development of an in-line monitoring strategy to support PAT-based real-time control actions and its integration into solid dosage manufacturing. All stages are discussed in this paper, from process analysis and definition of the monitoring task to technology assessment and selection, its process integration and the development of data acquisition.

Enhancement of sewage sludge thickening and energy self-sufficiency with advanced process control tools in a full-scale wastewater treatment plant.

On their path to becoming sustainable facilities, it is required that wastewater treatment plants reduce their energy demand, sludge production, and chemical consumption, as well as increase on-site power generation. This study describes the results obtained from upgrading the sludge line of a full-scale wastewater treatment plant over 6 years (2015-2021) using three advanced process control strategies. The advanced process control tools were designed with the aim of (i) enhancing primary and secondary sludge thickening, (ii) improving anaerobic digestion performance, and (iii) reducing chemical consumption in the sludge line. The results obtained show that the use of advanced process control tools allows for optimising sludge thickening (increasing solids content by 9.5%) and anaerobic digestion (increasing both the removal of volatile solids and specific methane yield by 10%, respectively), while reducing iron chloride and antifoam consumption (by 75% and 53%, respectively). With the strategies implemented, the plant increased its potential energy self-sufficiency from 43% to 51% and reduced de-watered sludge production by 11%. Furthermore, the upgrade required a low investment, with a return of capital expense (CAPEX) in 1.98 years, which presents a promising and affordable alternative for upgrading existing wastewater treatment plants.

Bioreactor control systems in the biopharmaceutical industry: a critical perspective.

Industrial-scale bioprocessing underpins much of the production of pharmaceuticals, nutraceuticals, food, and beverage processing industries of the modern world. The profitability of these processes increasingly leverages the economies of scale and scope that are critically dependent on the product yields, titers, and productivity. Most of the processes are controlled using classical control approaches and represent over 90% of the industrial controls used in bioprocessing industries. However, with the advances in the production processes, especially in the biopharmaceutical and nutraceutical industries, monitoring and control of bioprocesses such as fermentations with GMO organisms, and downstream processing has become increasingly complex and the inadequacies of the classical and some of the modern control systems techniques is becoming apparent. Therefore, with increasing research complexity, nonlinearity, and digitization in process, there has been a critical need for advanced process control that is more effective, and easier process intensification and product yield (both by quality and quantity) can be achieved. In this review, industrial aspects of a process and automation along with various commercial control strategies have been extensively discussed to give an insight into the future prospects of industrial development and possible new strategies for process control and automation with a special focus on the biopharmaceutical industry. Supplementary Information: The online version contains supplementary material available at 10.1007/s43393-021-00048-6.

Enhancement of biological nutrient removal process with advanced process control tools in full-scale wastewater treatment plant.

One of the major challenges in existing WasteWater Treatment Plants (WWTPs) is to comply with the increasingly stringent nutrient discharge limits established by the competent authorities to enhance environmental protection, while keeping operation costs as low as possible. This paper describes the results obtained from upgrading a full-scale WWTP during seven years (2013-2020) applying five different Advanced Process Control (APC) strategies. Results show that implementation of APC and the development of ammonia-based aeration control, aeration/non-aeration cycles, improved internal/external recirculation and chemical dosage controls resulted in an improvement in nutrients removal rates (+25.48% and +9.25%, for nitrogen and phosphorus, respectively) and in a reduction (-21.79%) of the specific energy ratio. In addition, the promotion of an Enhanced Biological Phosphorous Removal (EBPR) process by APC resulted in an improvement in biological phosphorous removal (+43.90%), chemical savings (-20.00%) and nutrient recovery in the dewatered sludge. Molecular biology tools and laboratory batch tests confirmed the Polyphosphate Accumulating Organisms (PAOs) activity, specifically Tetrasphaera genera. Finally, an economic analysis was conducted, showing a rate of return for the incurred capital expenses with the implemented APC systems of about five years, being an affordable alternative to the upgrading existing WWTPs.

Neural network identification in nonlinear model predictive control for frequent and infrequent operating points using nonlinearity measure.

In this paper, the problem of optimal system identification in nonlinear model predictive control (NMPC) for highly nonlinear dynamic processes is presented. Due to the short term changes in the operating point, the process may escape from its frequent operating points (FOP) to some infrequent operating points (IOP) for a short period. On the other hand, because the nonlinear model is identified using the operating data, it is mainly accurate for the FOP. Therefore, the NMPC causes tracking error or even instability in the IOP. To handle this problem, in this paper, we present a novel optimal identification algorithm, which is highly depended on the nonlinearity of the understudy plant, to train the nonlinear model of the NMPC. The nonlinear model is selected as a multi-layer perceptron neural network (MLP) which is trained to describe the nonlinear behaviour of the nonlinear dynamic system accurately in the FOP and while it has acceptable performance in the IOP. To validate the proposed algorithm, the well-known nonlinear dynamic pH neutralization process is chosen in both simulation and implementation parts. Finally, the simulation and implementation results prove the effectiveness of the proposed algorithm.

A Visualization and Control Strategy for Dynamic Minimization of Chemical Process Releases.

Our societal needs for greener, economically viable products and processes have grown given the adverse environmental impact and unsustainable development caused by human activities, including chemical releases, exposure, and impacts. To make chemical processes safer and more sustainable, a novel sustainability-oriented control strategy is developed in this work. This strategy enables the incorporation of online sustainability assessment and process control with sustainability constraints into chemical process operations. Specifically, U.S. Environmental Protection Agency (EPA)'s GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator) tool is used for sustainability assessment and environmental release minimization of chemical processes. The multivariable GREENSCOPE indicators in real time can be represented using a novel visualization method with dynamic radar plots. The analysis of the process dynamic behavior in terms of sustainability performance provides means of defining sustainability constraints for the control strategy to improve process sustainability aspects with lower scores. For the control tasks, Biologically Inspired Optimal Control Strategy (BIO-CS) is implemented with sustainability constraints so that the control actions can be calculated considering the sustainability performance. This work leads to a significant step forward towards augmenting the capability of process control to meet future demands on multiple control objectives (e.g., economic, environmental, and safety related). The effectiveness of the proposed framework is illustrated via two case studies associated with a fermentation system. The results show that the proposed control strategy can effectively drive the system to the desired setpoints while meeting a preset sustainability constraint and improving the transient sustainability performance by up to 16.86% in terms of selected GREENSCOPE indicators.

Towards a novel continuous HME-Tableting line: Process development and control concept.

The objective of this study was to develop a novel closed-loop controlled continuous tablet manufacturing line, which first uses hot melt extrusion (HME) to produce pellets based on API and a polymer matrix. Such systems can be used to make complex pharmaceutical formulations, e.g., amorphous solid dispersions of poorly soluble APIs. The pellets are then fed to a direct compaction (DC) line blended with an external phase and tableted continuously. Fully-automated processing requires advanced control strategies, e.g., for reacting to raw material variations and process events. While many tools have been proposed for in-line process monitoring and real-time data acquisition, establishing real-time automated feedback control based on in-process control strategies remains a challenge. Control loops were implemented to assess the quality attributes of intermediates and product and to coordinate the mass flow rate between the unit operations. Feedback control for the blend concentration, strand temperature and pellet thickness was accomplished via proportional integral derivative (PID) controllers. The tablet press hopper level was controlled using a model predictive controller. To control the mass flow rates in all unit operations, several concepts were developed, with the tablet press, the extruder or none assigned to be the master unit of the line, and compared via the simulation.

History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance.

Biological systems are typically composed of highly interconnected subunits and possess an inherent complexity that make monitoring, control and optimization of a bioprocess a challenging task. Today a toolset of modeling techniques can provide guidance in understanding complexity and in meeting those challenges. Over the last four decades, computational performance increased exponentially. This increase in hardware capacity allowed ever more detailed and computationally intensive models approaching a "one-to-one" representation of the biological reality. Fueled by governmental guidelines like the PAT initiative of the FDA, novel soft sensors and techniques were developed in the past to ensure product quality and provide data in real time. The estimation of current process state and prediction of future process course eventually enabled dynamic process control. In this review, past, present and envisioned future of models in biotechnology are compared and discussed with regard to application in process monitoring, control and optimization. In addition, hardware requirements and availability to fit the needs of increasingly more complex models are summarized. The major techniques and diverse approaches of modeling in industrial biotechnology are compared, and current as well as future trends and perspectives are outlined.

Recent progress in continuous manufacturing of oral solid dosage forms.

Continuous drug product manufacturing is slowly being implemented in the pharmaceutical industry. Although the benefits related to the quality and cost of continuous manufacturing are widely recognized, several challenges hampered the widespread introduction of continuous manufacturing of drug products. Current review presents an overview of state-of-the art research, equipment, process analytical technology implementations and advanced control strategies. Additionally, guidelines and regulatory viewpoints on implementation of continuous manufacturing in the pharmaceutical industry are discussed.

Detailed modeling and advanced control for chemical disinfection of secondary effluent wastewater by peracetic acid.

Advanced control of chemical disinfection processes is becoming increasingly important in view of balancing under-treatment (low pathogen inactivation) and over-treatment (excessive consumption of disinfectant and disinfection byproducts formation) thereby providing considerable environmental and economic benefits. Conventional control strategies such as flow pacing or residual trim ignore chemical demand/decay, inactivation kinetics, and other factors governing disinfection performance in continuous-flow reactors such as reactor hydraulics and process variability. This study presents the development, verification, and pilot-scale validation of a novel CT-based real-time disinfection control strategy, derived from first principles, and applied to peracetic acid disinfection of municipal secondary effluent wastewater. Validation experiments were carried out using a 3-m3 pilot contact basin of which the hydraulic performance was first characterized by means of tracer tests and then mathematically modeled using the well-established theoretical framework of continuous stirred-tank reactors in series. The analytical model describing hydraulic performance was subsequently extended to take into account disinfectant demand/decay and microbial inactivation kinetics. The integrated model was successfully used to predict, and control, residual peracetic acid as well as microbial concentration in the pilot effluent. Validation studies conclusively supported that the novel CT-based control strategy was superior in maintaining constant disinfection performance, desired microbial counts, and low residual disinfectant under variable flow and wastewater quality. When compared with flow pacing, the CT-based control required two times less the amount of chemical for the same treatment objective (<100 cfu/100 mL). Remarkably, the CT-based control strategy could be extended to other chemical disinfection processes such as chlorination and ozonation, alone or in combination with physical treatment technologies such as membranes and ultraviolet irradiation.

Standards-based integration of advanced process control and optimization.

Integration of process control with optimization is critical to Smart Manufacturing (SM). Oftentimes, however, the process control solutions from one vendor do not interoperate with the optimization solutions of another. Incompatibilities among the representation and format used by the vendors can impede interoperability. Without this interoperability, it is impossible to achieve the higher level of decision support essential to SM. We believe that an emerging standard, ISO 15746, can facilitate semantic interoperability and enable the integration of process control with optimization. This paper reports the implementation and validation of ISO 15746, Automation systems and integration - Integration of advanced process control and optimization (APC-O) capabilities for manufacturing systems. Guided by the standard, we modelled major components of a typical APC-O system using tools from different vendors, implemented the information models defined in the standard, and integrated key system functions such as process optimization, process control, and user interface. A chemical process case based on the Tennessee-Eastman problem is used to demonstrate the implementation and validation of the standard. We developed a simulation of the chemical process and integrated it with the APC-O system. We discuss the standard validation experience and the findings will be used to guide advance development of the standard.

On-Line Control of Glucose Concentration in High-Yielding Mammalian Cell Cultures Enabled Through Oxygen Transfer Rate Measurements.

Glucose control is vital to ensure consistent growth and protein production in mammalian cell cultures. The typical fed-batch glucose control strategy involving bolus glucose additions based on infrequent off-line daily samples results in cells experiencing significant glucose concentration fluctuations that can influence product quality and growth. This study proposes an on-line method to control and manipulate glucose utilizing readily available process measurements. The method generates a correlation between the cumulative oxygen transfer rate and the cumulative glucose consumed. This correlation generates an on-line prediction of glucose that has been successfully incorporated into a control algorithm manipulating the glucose feed-rate. This advanced process control (APC) strategy enables the glucose concentration to be maintained at an adjustable set-point and has been found to significantly reduce the deviation in glucose concentration in comparison to conventional operation. This method has been validated to produce various therapeutic proteins across cell lines with different glucose consumption demands and is successfully demonstrated on micro (15 mL), laboratory (7 L), and pilot (50 L) scale systems. This novel APC strategy is simple to implement and offers the potential to significantly enhance the glucose control strategy for scales spanning micro-scale systems through to full scale industrial bioreactors.

A membrane-integrated advanced scheme for treatment of industrial wastewater: dynamic modeling towards scale up.

Modeling and simulation was carried out for an advanced membrane-integrated hybrid treatment process that ensures reuse of water with conversion and recovery of ammoniacal nitrogen as value-added struvite fertilizer from coke wastewater. While toxic cyanide was largely removed in a pre-chemical treatment unit using Fenton's reagents under optimized conditions, more than 95% of NH4(+)-N could be recovered as a valuable by-product called struvite through addition of appropriate doses of magnesium and phosphate salts. Water could be turned reusable through a polishing treatment by nanofiltration membranes in a largely fouling free membrane module following a biodegradation step. Mathematical modeling of such an integrated process was done with Haldane-Andrew approach for the associated microbial degradation of phenol by Pseudomonas putida. Residual NH4(+) was degraded by nitrification and denitrification following the modified Monod kinetics. The model could successfully predict the plant performance as reflected in reasonably low relative error (0.03-0.18) and high Willmott d-index (>0.98).

Reator anaeróbio tipo UASB conjugado com processo Fenton para remoção de cor e demanda química de oxigênio de água residuária sintética de indústria têxtil / Anaerobic reactor type UASB conjunction with process Fenton to remove color and chemical oxygen demand textile industry of synthetic wastewater

RESUMO A contaminação por despejos de efluentes industriais têxteis tem sido uma preocupação emergente de pesquisadores e ambientalistas, pois esses apresentam composição extremamente heterogênea e grande quantidade de material tóxico e recalcitrante, o que dificulta seu tratamento. Durante o processamento têxtil, uma ampla gama de corantes é liberada e alguns desses, como os azo corantes, que se caracterizam pela função azo (-N=N-) ligada a grupos aromáticos e podem ser tóxicos, carcinogênicos e/ou mutagênicos. Em vista disso, esta pesquisa teve como principal objetivo avaliar os benefícios da utilização de um reator anaeróbio tipo reator anaeróbico de fluxo ascendente com manta de lodo (UABS), seguido de processo oxidativo avançado (POA) do tipo Fenton na degradação de cor e demanda química de oxigênio (DQO) de efluente sintético de indústria têxtil. Com os resultados, foram verificadas remoções de DQO em torno de 82,0% para o reator UASB e de 95,6% para o conjunto. A cor alcançou 96,1% de remoção no reator UASB e 100,0% ao final do processo.

ABSTRACT Contamination by textile industrial wastewater discharges has been an emerging concern of researchers and environmentalists, as they have extremely heterogeneous composition and loads of toxic and recalcitrant material, which complicates treatment. In the textile processing, a wide range of dye is released and some of these, such as dyes, azo, characterized by the feature azo (-N=N-) attached to aromatic groups and may be toxic, carcinogenic and/or mutagenic. In view of this, this research aimed to evaluate the benefits of using an anaerobic reactor type anaerobic reactor upflow sludge blanket (UABS), followed by advanced oxidation process (AOP) type Fenton in color degradation and chemical oxygen demand (COD) of synthetic textile industry effluent. From the results of COD removal was observed at around 82.0% for the UASB reactor and 95.6% for the group. The color reached 96.1% removal in UASB reactor and 100.0% at the end of the process.