Process Control Strategies Research Articles

Enhanced AmB Production in Streptomyces nodosus by Fermentation Regulation and Rational Combined Feeding Strategy.

Amphotericin B is a clinically important polyene macrolide antibiotic with a broad-spectrum antifungal activity. In this work, the addition of key precursors and differential metabolites, combined with staged fermentation process control strategies, was carried out to improve AmB production. Rationally designed addition strategies were proposed as follows: 4 mg/L isopropanol, 1 mM alanine, 1 g/L pyruvate, and 0.025 g/L nicotinamide were supplemented at 24 h. The AmB titer was ultimately enhanced to 6.63 g/L, with 28.5% increase in shake flasks fermentation. To further promote the biosynthesis of AmB, different glucose feeding strategies were investigated and the highest AmB titer (15.78 g/L) was obtained by constant speed fed-batch fermentation in a 5-L fermentor. Subsequently, compared with the batch fermentation (9.89 g/L), a novel combined feeding strategy was ultimately developed to improve the production of AmB by 85.9%, reaching 18.39 g/L that is the highest titer of AmB ever reported so far, in which the optimized components were fed at 24 h and the staged fermentation regulation strategies were used simultaneously. Moreover, the ratio of co-metabolite AmA decreased by 32.3%, from 3.1 to 2.1%. Through the detection of extracellular organic acids, the changes in α-ketoglutaric acid, pyruvate, and citric acid concentrations were identified as the most flexible metabolite nodes to further clarify the potential mechanism under different fermentation regulation strategies. These results demonstrated that the strategies above may provide new guidance for the industrial-scale production of AmB.

A Virtual Plant for Integrated Continuous Manufacturing of a Carfilzomib Drug Substance Intermediate, Part 2: Enone Synthesis via a Barbier-Type Grignard Process

This article details efforts to characterize and develop a process control strategy for the manufacture of enone 2, a carfilzomib drug substance intermediate obtained through a Barbier-type Grignar...

A Virtual Plant for Integrated Continuous Manufacturing of a Carfilzomib Drug Substance Intermediate, Part 3: Manganese-Catalyzed Asymmetric Epoxidation, Crystallization, and Filtration

This article describes the process characterization and development of models to inform a process control strategy to prepare (R,R)-epoxy ketone 2, an intermediate in the manufacture of carfilzomib...

A Virtual Plant for Integrated Continuous Manufacturing of a Carfilzomib Drug Substance Intermediate, Part 1: CDI-Promoted Amide Bond Formation

This manuscript details process characterization efforts and the development of corresponding process models to inform a process control strategy to produce a carfilzomib drug substance intermediat...

Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing.

Continuous manufacturing, an emerging technology in the pharmaceutical industry, has the potential to increase the efficiency, and agility of pharmaceutical manufacturing processes. To realize these potential benefits of continuous operations, effectively managing materials, equipment, analyzers, and data is vital. Developments for continuous pharmaceutical manufacturing have led to novel technologies and methods for processing material, designing and configuring individual equipment and process analyzers, as well as implementing strategies for active process control. However, limited work has been reported on managing abnormal conditions during operations to prevent unplanned deviations and downtime and sustain system capabilities. Moreover, although the sourcing, analysis, and management of real-time data have received growing attention, limited discussion exists on the continued verification of the infrastructure for ensuring reliable operations. Hence, this work introduces condition-based maintenance (CBM) as a general strategy for continually verifying and sustaining advanced pharmaceutical manufacturing systems, with a focus on the continuous manufacture of oral solid drug products (OSD-CM). Frameworks, such as CBM, benefit unified efforts towards continued verification and operational excellence by leveraging process knowledge and the availability of real-time data. A vital implementation consideration for manufacturing operations management applications, such as CBM, is a systems architecture and an enabling infrastructure. This work outlines the systems architecture design for CBM in OSD-CM and highlights sample fault scenarios involving equipment and process analyzers. For illustrative purposes, this work also describes the infrastructure implemented on an OSD-CM testbed, which uses commercially available automation systems and leverages enterprise architecture standards. With the increasing digitalization of manufacturing operations in the pharmaceutical industry, proactively using process data towards modernizing maintenance practices is relevant to a single unit operation as well as to a series of physically integrated unit operations.

DINAMIKA DAN PENGENDALIAN SISTEM QUADRUPLE TANK MENGGUNAKAN CONTROLLER PI-PID DENGAN METODE DETUNING

In general, industrial processes have characteristics as multivariable processes where the variables interact with one another which causes poor performance. Quadruple tank is a Multi Input Multi Output (MIMO) system that can be used to analyze dynamics and process control schemes with strong interactions. The design is simple but can represent complex systems in the industry so that a reliable controller can be tested using this system. Tuning controller parameters using Ziegler-Nichols only work well for single loops, therefore detuning controllers are needed using the BLT and Mc Avoy methods. The research method is to make mathematical models and determine the specifications of quadruple tanks. Next make a process simulation and identify the transfer function with the step test method. Interaction analysis using the Relative Gain Array (RGA) method. Tuning controller parameters using the Ziegler-Nichols method is then detuned using the BLT and Mc Avoy methods. Simulation of quadruple tank systems both open loop and closed loop can run well and can be used to test the reliability of PI-PID controllers with BLT and Mc Avoy detuning. Based on IAE values, controller with Mc Avoy detuning are superior compared to controllers with BLT detuning, both at level and temperature controllers.

Process Control Strategies in Chemical Looping Gasification—A Novel Process for the Production of Biofuels Allowing for Net Negative CO2 Emissions

Chemical looping gasification (CLG) is a novel gasification technique, allowing for the production of a nitrogen-free high calorific synthesis gas from solid hydrocarbon feedstocks, without requiring a costly air separation unit. Initial advances to better understand the CLG technology were made during first studies in lab and bench scale units and through basic process simulations. Yet, tailored process control strategies are required for larger CLG units, which are not equipped with auxiliary heating. Here, it becomes a demanding task to achieve autothermal CLG operation, for which stable reactor temperatures are obtained. This study presents two avenues to attain autothermal CLG behavior, established through equilibrium based process simulations. As a first approach, the dilution of active oxygen carrier materials with inert heat carriers to limit oxygen transport to the fuel reactor has been investigated. Secondly, the suitability of restricting the air flow to the air reactor in order to control the oxygen availability in the fuel reactor was examined. Process simulations show that both process control approaches facilitate controlled and de-coupled heat and oxygen transport between the two reactors of the chemical looping gasifier, thus allowing for efficient autothermal CLG operation. With the aim of inferring general guidelines on how CLG units have to be operated in order to achieve decent synthesis gas yields, different advantages and disadvantages associated to the two suggested process control strategies are discussed in detail and optimization avenues are presented.

Time domain modeling and control of complex non-linear chemical processes using relay feedback test

This study analyzes with the technology of auto tuning using relay feedback test for non-square MIMO system through process modeling, identification, input-output pairing and control strategies. However, the control configuration selection based on conventional steady-state Relative Gain Array (RGA) matrix sometimes degrades the loop performance and it needs attention. This study also deals with the real challenges in time domain modeling and appropriate pairing of loops for non-linear chemical processes. The choice of input-output pairing for square system has been extended to non-linear chemical processes. This ensures the system’s stability by selecting an appropriate manipulated-controlled variable pairing in non-linear chemical processes and the same is also tested for benchmark of non-linear chemical processes. In this study, two types of non-square systems are considered: one is systems with excess input than output variables and the other is systems with excess output than input variables. Some benchmark non-linear chemical processes, such as processes with mild nonlinearity, moderate to high nonlinearity and highly nonlinearity, are also taken for this study. Three standard benchmark processes are used in analysis of nonlinear chemical processes, namely: (1) Continuous Stirred Tank Reactor (CSTR) process; (2) hydrogen-ion- concentration (pH) process and (3) distillation column and this procedure is illustrated via simulation of 3-by-2 and 2-by-3 non-linear chemical processes.

Effect of reduced gas pressure on yield of biogas and other physicochemical parameters

The internal gas pressure is one of the major parameters affecting the microclimate of the anaerobic digester. The effect can be described by Henry’s Law and Ideal Gas law. In this experiment, the effect of reduced gas pressure on the yield of biogas and its composition was observed. The pressure of gas was reduced by displacement of a piston connected to the balancing weight, to maintain constant reduced pressure. As the piston is displaced to its maximum point due to gas generation, then the gas is transferred to the gas storage tank for further analysis. The gauge pressure applied was 0, $$-100$$ , and $$-200$$ mmHg for systems A, B, and C, respectively. The specific gas yield of systems A, B and C was 26.09, 70.41, and 246.5 L/kg (on dry basis). The production of methane was increased with a decrease in internal gas pressure. The corresponding average methane content of systems A, B and C was 38.33, 44 and 63.97%. The $$\hbox{BOD}_{5}$$ removal efficiencies were 6.25, 33.33 and 70.83% for systems A, B, and C, respectively. The pH of the substrate was stable in the system C. The rate of Total Solid (TS) reduction was high in System C. The TS reduction efficiency of systems A, B, and C was 14.7, 16.64, and 22.19%, respectively. The reduction in internal gas pressure can be a new effective and efficient process control strategy. The reduced-pressure system reduces the chemical cost incurred by the addition of alkalinity to maintain the pH.

Working within the Design Space: Do Our Static Process Characterization Methods Suffice?

The Process Analytical Technology initiative and Quality by Design paradigm have led to changes in the guidelines and views of how to develop drug manufacturing processes. On this occasion the concept of the design space, which describes the impact of process parameters and material attributes on the attributes of the product, was introduced in the ICH Q8 guideline. The way the design space is defined and can be presented for regulatory approval seems to be left to the applicants, among who at least a consensus on how to characterize the design space seems to have evolved. The large majority of design spaces described in publications seem to follow a “static” statistical experimentation and modeling approach. Given that temporal deviations in the process parameters (i.e., moving within the design space) are of a dynamic nature, static approaches might not suffice for the consideration of the implications of variations in the values of the process parameters. In this paper, different forms of design space representations are discussed and the current consensus is challenged, which in turn, establishes the need for a dynamic representation and characterization of the design space. Subsequently, selected approaches for a dynamic representation, characterization and validation which are proposed in the literature are discussed, also showcasing the opportunity to integrate the activities of process characterization, process monitoring and process control strategy development.

Modeling of a full-scale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network

A long short-term memory (LSTM)-based neural network was proposed and developed to predict the ammonium (NH4-N), total nitrogen (TN), and total phosphorus (TP) removal efficiency of an anaerobic-anoxic-oxic membrane bioreactor (A-A-O MBR) system after data visualization using Python programming. The influent wastewater characteristics, including, total organic contents, NH4-N, TN, TP, chemical oxygen demand, suspended solids, and operating parameters, such as dissolved oxygen, oxidation-reduction potential, and mixed-liquor suspended solids, were considered as inputs, while removal efficiency was taken as an output parameter. First, data analysis and its normalization were conducted to improve the learning speed of the model. Performance criteria of the proposed model were evaluated based on statistical values, including the mean-square error (MSE) and root-mean-square error-observations standard deviation ratio (RSR). Based on the goodness-of-fit values, the proposed LSTM model achieved good performance in the testing dataset; calculation results deviated little, indicated by the MSE values of 0.0047, 0.015, and 0.018 for NH4-N, TN, and TP, and RSR values of 0.0104, 0.129, and 0.141, respectively. The proposed LSTM model predicted the most precise removal efficiency for NH4-N of A-A-O MBR system, while TN and TP predictions were comparatively less accurate, but still acceptable. The proposed LSTM model is promising for predicting the nutrient removal efficiency of the A-A-O MBR system in real-time and can aid in establishing process control strategies. Therefore, the proposed LSTM model is an adequate interpolation tool to predict the nutrient removal efficiency of the A-A-O MBR process in wastewater treatment systems.

Bioreactor Simulation with Quadratic Neural Network Model Approximations and Cost Optimization with Markov Decision Process

The efficient operation of activated sludge type wastewater treatment plants is an ongoing topic for the utility providers, where electric energy consumption shares are high, giving cca. 30 % of total operational costs. Intervention methods for intensification include fine tuning of aeration settings, sludge removal and the adjustment of recirculation rates. In order to analyze the effects of various process control strategies, activated sludge models (ASM) are used for the purpose of biokinetic modeling. In practice, most model simulators do not incorporate optimization and necessary auto-calibration of the latter, due to high computational demand of timeseries evaluation. In this paper, a new mathematical model is presented, which makes biokinetic simulations suitable for the use in decision support systems. Namely, the ASM model is approximated with a computanional inexpesive quadratic model solution, fed into a set of mass-balance corrected neural networks. Cost optimization is achieved with Markov decision process model. The developed method was illustrated for a case of Hungarian, large wastewater treatment plant. It was proven, the model is able to find better aeration schemes for the plant in aspect of cost of operation and nitrogen removal efficiency. The model can be used to find cost-optimal policies under arbitrary defined conditions. As a benefit, results can be implemented into industrial logic controllers.

Partial nitrification-reactor configurations, and operational conditions: Performance analysis

• Essential parameters influencing partial nitrification were evaluated. • Bioreactors configurations and control strategies for nitritation were summarized. • Up-to date full-scale application of partial nitrification was demonstrated. • Research gaps for partial nitrification was presented and discussed. This comprehensive literature analysis evaluated essential partial nitrification (PN) parameters including various reactor configurations, control strategies on dissolved oxygen (DO) and aeration, pH, inhibition via free ammonia (FA) and free-nitrous acid (FNA), and oxidation reduction potential (ORP) in both pilot and full-scale systems. The median growth rates for ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) at 20 °C are 0.74 and 0.65 d −1 , respectively. The median first-order decay coefficient for both AOB and NOB was 0.14 d −1 while the average biomass yields were 0.17 and 0.08 mg chemical oxygen demand (COD) mg N −1 . The COD/N ratio is one of the critical factors for partial nitrification, particularly for attached growth systems. The optimum COD/N ratio for partial nitrification appears to be system-specific, as it is impacted both by system design parameters and operational conditions. The most widely used PN technology is the sequencing batch reactor (SBR) despite significantly lower volumetric nitrogen loading rates due to the ease of NOB suppression by the relatively high FA and FNA concentrations during the early stage of aeration. This review identified several knowledge gaps including better understanding of PN mechanisms in attached growth systems, combination of PN and enhanced biological phosphorous removal (EBPR), and development of process control strategies for technologies other than SBRs. Mainstream low-temperature application and combination with EBPR, particularly in light of the inhibitory impact of nitrites on phosphorus accumulating bacteria, are two major challenges hindering the widespread application of partial nitrification.

Thin wall deposition of IN625 using directed energy deposition

Abstract Laser powder-blown directed energy deposition (DED) is widely used for small feature deposition ability and geometrical accuracy. The effects of process parameters, control, and toolpath strategies on geometry of the final product and mechanical properties have been investigated in many different parent geometries, however, deposition on an existing thin feature that has less width than the diameter of the laser spot size has yet to be addressed and is critical for various component repair applications. This study quantifies controllable factors contributing to optical characterization, geometry, characteristics of internal porosity, and microstructure of the deposit in the deposition processes of Inconel 625 by the laser powder-blown DED method. A full-factorial experimental plan was arranged with variations in laser power (175 W to 275 W) and mass flowrate (2 g/min to 20 g/min) on thin (0.5 mm) and thick (3.0 mm) substrate deposition. These results are useful for deriving an optimal workpiece quality through adjustment of controllable process parameters.

An improved 2-degree-of-freedom internal model proportional–integral–derivative controller design for stable time-delay processes

In this article, an enhanced 2-degree-of-freedom internal model control strategy for typical industrial processes with time-delay is developed. For the proposed controller, it is composed of an inner loop feedback controller which is designed based on the internal model control theory and a weighted set-point tracking controller. Note that the adjustment of set-point tracking performance and disturbance rejection characteristics can be decoupled by employing the developed strategy, which indicates that more degrees of freedom are obtained for the proposed controller design; thus, better ensemble performance and stronger robustness are anticipated by regulating these two controllers separately, which may not be achieved in the conventional internal model control method. Case studies on two kinds of stable processes with time-delay verify the effectiveness of the proposed scheme finally.

Model Predictive Control-Based Smart Linear Servo-Motor Driver for a Resonance Frequency Tuner of Azimuth Variable Field Cyclotrons

The most important thing in the startup procedure or the long-term operation of radio frequency (RF) cyclotrons especially azimuth variable field (AVF) cyclotrons, is the resonance frequency stability of the cavities. Generally, servo-motors or stepper motors are used to adjust the tuners of cavities. Actually, proportional-integral-derivative (PID) controller-based servo-motor drivers have many problems for long-term operation, such as the tuner’s temperature dependence, shot noise, and so on. Our model predictive controller (MPC) or MPC-based smart linear servo-motor driver in a multi-input and single-output state, with artificial intelligence (AI), can solve usual and unusual problems in order to control the speed and/or position. In other words, the MPC is a progressive strategy of process control to control the process and provides satisfaction limitations. Additionally, this real-time controller can reduces the time needed for the startup procedure and minimizes the number of failures during long-term operation.

Controlled Inline Fluid Separation Based on Smart Process Tomography Sensors

AbstractToday's mechanical fluid separators in industry are mostly operated without any control to maintain efficient separation for varying inlet conditions. Controlling inline fluid separators, on the other hand, is challenging since the process is very fast and measurements in the multiphase stream are difficult as conventional sensors typically fail here. With recent improvement of process tomography sensors and increased processing power of smart computers, such sensors can now be potentially used in inline fluid separation. Concepts for tomography‐controlled inline fluid separation were developed, comprising electrical tomography and wire‐mesh sensors, fast and massive data processing and appropriate process control strategy. Solutions and ideas presented in this paper base on process models derived from theoretical investigation, numerical simulations and analysis of experimental data.

Double-Loop Multi-Scale Control using Routh-Hurwitz Dimensionless Parameter Tuning for MIMO Processes

This paper presents a new approach to controlling MIMO processes by using the double-loop multi-scale control scheme in the decentralized control architecture. The decentralized PID control system has been used in process industry despite its several limitations due to process interactions, time-delays and right half plane poles. To overcome the performance limitation due to process interactions, decoupling controllers are often added to the decentralized PID control system. The proposed strategy based on the double-loop multi-scale control scheme has some advantages over the existing control strategies for MIMO processes. An advantage of the proposed scheme over the decentralized PID control with decoupling system is that, the proposed strategy has a fixed number of dimensionless tuning parameters that are easy to tune. For an n×n MIMO process, the proposed scheme requires the tuning of only 3 to 6 dimensionless parameters instead of the 3n original PID parameters.

Effects of ozonation on halogenated acetaldehydes and trihalomethanes formation: Strategy of process control for a full-scale plant

Ozonation provides several advantages during the drinking water treatment process, including controlling the formation of regulated disinfection by-products (DBPs) such as trihalomethanes (THMs) and haloacetic acids (HAAs). However, ozonation can lead to the formation of bromate and unregulated halogenated acetaldehydes (HALs). In order to better understand the effects of ozonation on the formation and speciation of different DBPs and on the quality of drinking water, high-frequency water quality monitoring was conducted in a Canadian full-scale drinking water treatment plant (DWTP). We collected samples during a period when the DWTP underwent changes to their ozonation strategies: applying only post-ozonation (Post-O3) and only pre-ozonation (Pre-O3), and a transition period comprising two-stage (pre- and post-) ozonation and no-ozonation. In this case study, four different phases of disinfection were examined chronologically: Phase 1 (P1), Post-O3 with Post-Cl2; Phase 2 (P2), Pre-O3 and Post-O3 with Post-Cl2; Phase 3 (P3), Post-Cl2 only; Phase 4 (P4), Pre-O3 with Post-Cl2. In finished water, we observed that ozonation significantly effected the removal of total nitrogen (TN) and aromatic content (UV-254, SUVA) only, but not the removal of TOC. Total ozone dosage (Pre-O3 + Post-O3) appeared to be a contributing factor for HAL formation. Compared to non-ozonated water (chlorination only), concentrations of chloral hydrate (CH, the most prevalent HAL) were double in ozonated water. Dibromoacetaldehyde (DBAL) formed only in the presence of pre-ozonation. Ozonation effectively reduced the formation of total THMs, whereas more brominated THMs were formed with higher ozone dosages.

Modelling energy efficiency and generation potential in the South African wastewater services sector.

About 55% of energy used in the South African water cycle is for wastewater treatment, with the bulk of this energy associated with aeration in biological processes. However, up to 15% of wastewater energy demand can be offset by energy generation from sludge (power and/or combined heat and power), while best practices adoption can deliver energy efficiency gains of between 5% and 25% in the water cycle. Advanced process modelling and simulation has been applied in this study as a tool to evaluate optimal process and aeration control strategies. This study further applied advanced modelling to investigate and predict the potential energy consumption and consumption cost pattern by the South African wastewater sector resulting from implementation of optimal process and aeration energy use reduction strategies in support of sustainable municipal wastewater management. Aeration energy consumption and cost savings of 9-45% were demonstrated to be achievable through implementation of energy conservation measures without compromising final effluent regulatory compliance. The study further provided significant potential future energy savings as high as 50% and 78% through implementation of simple and complex aeration energy conservation measures respectively. Generally, the model-predicted energy savings suggest that adoption of energy efficiency should be coupled with electricity generation from sludge in order to achieve maximum energy consumption and cost savings within the South African wastewater services sector.