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The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

Advances in continuous manufacturing in the pharmaceutical industry necessitate reliable process monitoring systems that are capable of handling measurement errors inherent in all sensor technologies and detecting measurement outliers to ensure operational reliability. The purpose of this work was to demonstrate data reconciliation (DR) and gross error detection methods as real-time process management tools to accomplish robust process monitoring. DR mitigates the effects of random measurement errors, while gross error detection identifies nonrandom sensor malfunctions. DR is an established methodology in other industries (i.e., oil and gas) and was recently investigated for use in drug product continuous manufacturing. This work demonstrates the development and implementation of model-based steady-state data reconciliation on 2 different end-to-end continuous tableting lines: direct compression and dry granulation. These tableting lines involve different equipment and sensor configurations, with sensor network redundancy achieved using equipment-embedded sensors and in-line process analytical technology tools for the critical process parameters and critical quality attributes. The nonlinearity of the process poses additional challenges to solve the steady-state data reconciliation optimization problem in real time. At-line and off-line measurements were used to validate the framework results.

Flowsheet design and optimization constitute one of the key challenges in the chemical engineering and process optimization communities. Software tools for digital design and flowsheet simulation are readily available for traditional chemical processing problems such as distillation and hydrocarbon processing, however tools for pharmaceutical manufacturing are much less widely developed. This paper introduces, PharmaPy, a Python-based modelling platform for pharmaceutical facility design and optimization. The versatility of the platform is demonstrated in simulating continuous, batch, and hybrid process flowsheets.

As outlined in the Paris Agreement on climate change, efforts to mitigate and adapt to climate change will require new modes of development of the energy sector including the transformation and expansion of power systems to low-carbon and more resilient designs. However, there is a need for more systematic tools to support decision-making processes in the context of climate change impacts and adaptation strategies for the energy and power sectors. For instance, quantitative approaches should be developed and implemented for the assessment of the impacts and hedging strategies associated with the uncertainties inherent to energy and power planning problems. This study addresses the development and implementation of an integrated model-based system analysis, which uses general circulation models, global sensitivity analysis, and stochastic optimization techniques, for the optimal design and planning of the Colombian power system in view of submitted climate pledges and climate change adaptation. It was found that during the 2015 to 2029 time frame, climate change will likely reduce the capacity factor of hydropower generation by 5.5–17.1%. Additionally, it was established that the independent effects of three key uncertain parameters, i.e., capacity factor of hydropower generation, gas prices, and emission reduction target, account for ∼96% of the variance in the total cost for the required expansion and operation of the power system. Furthermore, when uncertainty is taken into account, the optimal expansion strategy consists of rescheduling of investments in hydropower plants and investing more in carbon management technologies and renewable power plants to compensate for the uncertainty in hydropower generation, climate policy, and gas prices.

The development of shale gas resources is subject to technical challenges and markedly affected by volatile markets that can undermine the development of new projects. Consequently, stakeholders can greatly benefit from decision‐making support tools that integrate the complexity of the system along with the uncertainties inherent to the problem. Accordingly, a general methodology is proposed in this work for the evaluation of integrated shale gas and water supply chains under uncertainty. First, key parametric uncertainties are identified from a candidate pool via a global sensitivity analysis based on a deterministic optimization model. Then, a two‐stage stochastic model is developed considering only the key uncertain parameters in the problem. Moreover, the merits of modeling uncertainty and implementing the stochastic solution approach are evaluated using the expected value of perfect information and the value of the stochastic solution metrics. Furthermore, the conditional value‐at‐risk approach was implemented to evaluate different risk‐aversion levels and the corresponding impacts on the shale gas development plan. The proposed methodology is illustrated through two real‐world case studies involving six and eight potential well‐pad locations and two options of well‐pad layouts.

PURPOSE: Reliable process monitoring in real-time remains a challenge for the pharmaceutical industry. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this paper, we present a process model-based framework, which for given sensor network and measurement uncertainties will predict the most likely state of the process. Thus, real-time process decisions, whether for process control or exceptional events management, can be based on the most reliable estimate of the process state. METHODS: Reliable process monitoring is achieved by using data reconciliation (DR) and gross error detection (GED) to mitigate the effects of random measurement errors and non-random sensor malfunctions. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. We also compare and contrast the model-based DR approach (SSDR-M) to the purely data-driven approach (SSDR-D) based on the use of principal component constructions. RESULTS: We report the results of studies on a pilot plant-scale continuous direct compression-based tableting line at steady-state in two subsystems. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. SSDR-M also complies with mass balances and estimate unmeasured variables. CONCLUSIONS: SSDR successfully estimates the true state of the process in presence of gross errors, as long as steady state is maintained and the redundancy requirement is met. Gross errors are also detected while using SSDR-M or SSDR-D. Process monitoring is more reliable while using the SSDR framework.

Shale gas has gained importance in the energy landscape in recent decades. However, its development has raised environmental concerns, especially, those associated with water management. Thus, the assessment of water management aspects, which inevitably impact the economic aspects, is crucial in evaluating the merits of any project exploiting this energy source. This paper provides a review of the economic and environmental implications of shale gas development around the world. Furthermore, to demonstrate the interplay between the various technical, environmental and economic factors in concrete terms, we report on a specific set of case studies conducted using an integrated decision-support tool that has been implemented to model and optimize shale gas development projects. The case study results confirm that the gas breakeven price decreases with expansion in scale of the shale gas development, i.e. increasing the number of well-pads in the system. However, scale also increases the options for water re-use and recycle in drilling and fracturing operations, which can result in lower freshwater withdrawal intensity. Moreover, under water scarcity scenarios, the choice of well-pad designs that are inherently less water intensive was found to be more cost-effective than water re-use or/and recycle strategies at reducing net freshwater demand. Similar trends were observed when the impact of wastewater quality, i.e. total dissolved solids concentration, on the optimal development strategy of shale gas plays was investigated. The results of these case studies reveal that greater efforts are needed at characterizing freshwater availability and wastewater quality for the evaluation of both the economic and environmental aspects of shale gas development.

State estimation is a fundamental part of monitoring, control, and real-time optimization in continuous pharmaceutical manufacturing. For nonlinear dynamic systems with hard constraints, moving horizon estimation (MHE) can estimate the current state by solving a well-defined optimization problem where process complexities are explicitly considered as constraints. Traditional MHE techniques assume random measurement noise governed by some normal distributions. However, state estimates can be unreliable if noise is not normally distributed or measurements are contaminated with gross or systematic errors. To improve the accuracy and robustness of state estimation, we incorporate robust estimators within the standard MHE skeleton, leading to an extended MHE framework. The proposed MHE approach is implemented on two pharmaceutical continuous feeding–blending system (FBS) configurations which include loss-in-weight (LIW) feeders and continuous blenders. Numerical results show that our MHE approach is robust to gross errors and can provide reliable state estimates when measurements are contaminated with outliers and drifts. Moreover, the efficient solution of the MHE realized in this work, suggests feasible application of on-line state estimation on more complex continuous pharmaceutical processes.

In this work, a methodology for the parameter estimation of heterogeneously-catalyzed reactions is presented. A simulation-optimization framework based on a dynamic model was coupled with an identifiability analysis, in order to detect for which parameters the dynamic model is most sensitive. The implemented identifiability analysis was based on rank-revealing matrix factorizations, with singular values as criteria for parameter selection. As the dynamic equation systems describing catalytic reactions are expected to be ill-posed, a subset selection step based on identifiability analysis was included. In order to illustrate the methodology, the ODE system describing the heterogeneously-catalyzed reaction system for the production of nopol from β-pinene and formaldehyde was used as a case study. After applying the methodology, two out of five kinetic parameters were found to be identifiable, consistent with a Langmuir Hinshelwood Hougen Watson (LHHW) mechanism that considers adsorption on catalytic sites of different nature. Confidence intervals of the estimated parameters belonging to the identifiable subset were not higher than 3% of the parameter value. The results of this work show that the proposed mechanism is capable of reproducing the dynamics of the reaction system, and are an important input for the design of a three-phase reactor for nopol production.

Energy systems face a growing vulnerability to the availability and quality of water sources as a consequence of rising energy demand and increasing climate variability. The vulnerability of energy systems to water utilization constraints could be mitigated by the effective design and implementation of water management strategies in energy conversion process and supply chain systems. Based on a broad literature review, this study provides a comprehensive examination of the recent advances in methodologies to support decision-making processes involving water management in the energy sector. Water management issues which require more attention by the research community, include: (i) development of decision-support models for biofuel supply chains that deal with water scarcity scenarios, (ii) integration of wastewater quality variability into the design and planning of water management strategies for the development of unconventional fossil fuels, (iii) improvements in the efficiency of cooling systems, and (iv) integration of decision-support tools with climate and weather models for the optimal design, planning, and operation of integrated water and energy supply chains, especially power systems. The systematic targeting of the aforementioned issues in the near future is critical and requires the joint efforts of the energy modeling as well as the weather and climate research communities, which to date have principally addressed water management issues from their own individual perspectives.

Demonstrate the use of a knowledge management and data curation (KProMS) system to support a collaborative research project involving the generation of extensive critical product quality data and the investigation of alternative statistical sampling/analysis strategies for product release. A suite of workflows was developed for the analytical testing and calibration activities associated with the required USP dissolution, HPLC, weight, hardness, and physical dimension measurements. The workflow library also includes the computational steps in the relevant Bayesian and frequentist statistical analyses. The necessary interfaces enabling the transfer of raw data from the instrument output files into KProMS were also implemented. By virtue of a HUB-based implementation of KProMS, all of the details of both laboratory and statistical procedures as well as the resulting data and analysis are web-accessible to all authorized participants. The system enabled data generation, sharing and harvesting over the web, and seamless integration of activities between groups located in three different locations. KProMS can be effectively used in routine management of the dosage form quality data in pharmaceutical operations. This would allow industrial laboratories to seamlessly generate data and populate the knowledgebase to track the analysis for product release and for correlation to process control charting.

The progress in continuous downstream manufacturing of oral solid doses demands effective real-time process management, with monitoring at its core. This study evaluates the feasibility of using a commercial sensor to measure the mass flow rate of the particulates, a critical process variable in continuous manufacturing. The sensor independently measures X-ray attenuation and cross-correlation velocimetry of particulate flow in real time. Steady-state flow rates of blends comprised primarily of acetaminophen and microcrystalline-cellulose are monitored using the sensor, with simultaneous weighing scale measurements, to calibrate the sensor and investigate the measurement accuracy. The free-fall flow measurement of the powder and granule blends in a conduit is linearly proportional to the X-ray attenuation. Relative standard deviations of ∼3%-7% for 1 s monitoring are observed and a measurement error of approximately 5% suggests the usability of the sensor for real-time monitoring. The sensor measurement is robust for operational variations in composition, addition of lubricant or glidant and reuse of material for PAT tool calibration. The measurement relative standard deviations depend on particulate flow dynamics at the sensor location. This requires experimental evaluation for a given material at the sensor location, to capture the flow dynamics of the particulate stream through the sensor.

The paradigm shift in the pharmaceutical industry to continuous manufacturing, which has recently progressed from conceptual demonstration to pilot production, has stimulated the development and application of process systems engineering (PSE) tools for implementing efficient and robust control strategies. In this study, a systematic framework for process control design and risk analysis for continuous pharmaceutical solid-dosage manufacturing is proposed, consisting of system identification with state-space models; control design and analysis metrics; hierarchical three-layer control structures; risk mapping, assessment and planning (Risk MAP) strategies; and control performance indicators. The framework is applied to a feeding-blending system, wherein the major source of variance in the product quality arises. It can be demonstrated that the variance in the feeding-blending system can be mitigated and managed through the proposed systematic framework for control design and risk analysis. The process analytical technology (PAT) tool for mass fraction measurement of active pharmaceutical ingredient (API) and its relative standard deviation (RSD) were indispensable to achieve an efficient control design at the advanced layers. Specifically, the improvements in control performance by implementing advanced model-based control strategy are found to be limited by model-plant mismatch and the sampling time of the PAT tools.

Real-time optimization (RTO) is an important requirement of pharmaceutical production processes. Production processes are operated by adjusting the setpoints of one or more individual unit operations, either to optimize a given objective function or to satisfy externally imposed constraints. Most commonly, setpoints are changed based on inputs from other components of a larger real-time process management (RTPM) system. Since setpoint changes may have knock-on effects elsewhere in the process, it is important to treat setpoint changes not as an isolated activity but as part of a more comprehensive process management suite. This chapter describes common manufacturing scenarios that require setpoints to be changed or optimized, discusses the trade-offs between static optimization based on a priori flowsheet simulation and dynamic optimization based on current process data, and describes good practices in such setpoint transitions.

Pharmaceutical continuous manufacturing has steadily progressed from the proof of concept to the pilot and industrial production in the past two decades, some of which have recently been approved by the US Food and Drug Administration (FDA), resulting in a greater demand on experience in process design and operation in pharmaceutical continuous manufacturing. Unlike many of the individual unit operations that are themselves continuous operations, such as roller compaction, tableting, etc., and have been well studied previously, only the characterization of a continuous feeding-blending system will be discussed in detail in this chapter, which undergoes the most substantial change with a transition from batch to continuous manufacturing.

Continuous manufacturing in the pharmaceutical industry is an emerging technology, although it is widely practiced in industries such as petrochemical, bulk chemical, foods, and mineral processing. This chapter briefly discusses the characteristics of continuous manufacturing at the conceptual level, first, in its generic form, viewing the process as a unitary system, and then as a system composed of multiple manufacturing unit operations. Key requirements for implementing an effective continuous process are reviewed, while aspects specific to pharmaceutical applications are highlighted. The advantages and limitations of continuous manufacturing are discussed and compared to the advantages and limitations of the batch operating mode, which has been the mainstay of the pharmaceutical industry. Perspectives on advancing pharmaceutical manufacturing in the Industry 4.0 era are discussed.

Pharmaceutical continuous manufacturing is essentially in a steady state, or in a state of control, in process operation, by which variations in critical material/product properties and process parameters can be monitored and controlled in real time within an acceptable range that enables the comprehensive implementation of the Quality by Design (QbD) principles. This advantage, facilitated by the implementation of active process control, has recently been evolving and nurturing a new paradigm of Quality by Control (QbC) in pharmaceutical continuous manufacturing. The concept of QbC has acquired acknowledgement in recent applications in pharmaceutical continuous manufacturing and bioprocessing.

Spray drying unit operation is generally used for separating and drying a solid that cannot be mechanically dried because cannot be exposed to high-temperature atmospheres for long periods. For this reason, spray dryers are related to heat-sensitive products like food or drugs but can be also used for natural extract encapsulation in order to increase their thermal stability. In this work, this last aspect was investigated and a model of co-current spray-drying, was developed and validated. This model is based on mass, energy and momentum balances and take into account of the distribution of the particle size. An experimental campaign was performed using a laboratory scale spray dryer (Buchi Mini Spray Dryer B-290, Switzerland) to validate the model. Maltodextrin and cyclodextrin were used as carrier to encapsulate grape skin and citrus extracts respectively. Different tests were done varying the operating condition of the spray dryer like the inlet air temperature (from 120°C to 180°C) and the mass ratio between carrier and natural extract. Simulation results and experimental data showed a good agreement in terms of mass yield and outlet temperature, while the outlet moisture content show slightly difference e needs to be further investigated.

Combined Heat and Power (CHP) and biomethane upgrading plants are the two main processes that use biogas in Europe. The first converts biogas into electric energy and heat while the second consist of the purification of methane, via removal of other components, and its injection into the national natural gas distribution grid. They both are considered to be green technologies but the overall carbon balance is positive for both processes. Use of biogas as raw material in chemical synthesis allows to fix carbon in the chemical molecule and avoid its release as carbon dioxide. This is the basic idea of Combined Heat Power and Chemical plants (CHPC). Starting from biogas, CHPC produces methanol, a valuable and important building block for industrial chemistry. In this work we optimized the entire process by using multiple objective functions (economics and environmental) and considering the uncertainty of the feed composition. The results of mono-objective optimization show that the CHPC plant can be economically feasible with a net consumption of CO2. Multiple objective optimization identified the operating conditions in which payback time is reasonable and CO2 balance negative. Optimization under uncertainty allowed to design a more flexible and realistic process which can accommodate the variations in inlet biogas composition.

One of the key objectives in precision medicine is to determine the right dose for the individual patient at the right time so that the desired therapeutic effect is achieved. The focus of this work is on modeling of pharmacokinetic/ pharmacodynamic data to facilitate the achievement of this goal. One novelty of our approach is to use structured models, such as physiologically-based compartment models and un-structured models, such as artificial neural networks or Gaussian Processes in a hierarchical fashion. The reason for using a hierarchical structure is that there are available well-established empirical compartmental and mechanistic physiologically based models, which do not explicitly account for various predictive covariates such as co-administered drugs or different laboratory measurements such as total protein, blood urea nitrogen, or urine output. Thus, we extend the structured models with the second hierarchical layer of an un-structured model and utilize the unstructured model to capture the effects of those covariates. Secondly, we employ Bayesian inference which allows direct quantification of uncertainty in the model predictions. Thirdly, utilization of Bayesian inference for the unstructured models (specifically Bayesian neural networks) allows the determination of important predictive covariates such as serum creatinine, blood urea nitrogen, or urine output.

Data provided by in situ sensors is always affected by some level of impreciseness as well as uncertainty in the measurements due to process operation disturbance or material property variance. In-process data precision and reliability should be considered when implementing active product quality control and real-time process decision making in pharmaceutical continuous manufacturing. Data reconciliation is an important strategy to address such imperfections effectively, and to exploit the data redundancy and data correlation based on process understanding. In this study, a correlation between tablet weight and main compression force in a rotary tablet press was characterized by the classical Kawakita equation. A load cell, situated at the exit of the tablet press chute, was also designed to measure the tablet production rate as well as the tablet weight. A novel data reconciliation strategy was proposed to reconcile the tablet weight measurement subject to the correlation between tablet weight and main compression force, in such, the imperfect tablet weight measurement can be reconciled with the much more precise main compression force measurement. Special features of the Welsch robust estimator to reject the measurement gross errors and the Kawakita model parameter estimation to monitor the material property variance were also discussed. The proposed data reconciliation strategy was first evaluated with process control open-loop and closed-loop experimental data and then integrated into the process control system in a continuous tablet manufacturing line. Specifically, the real-time reconciled tablet weight measurements were independently verified with an at-line Sotax Auto Test 4 tablet weight measurements every five minutes. Promising and reliable performance of the reconciled tablet weight measurement was demonstrated in achieving process automation and quality control of tablet weight in pilot production runs.

The Knowledge Provenance Management System, KProMS, can capture the complete provenance of the data, information, and knowledge of a structured activity by modeling the details of the associated data generation steps of that activity as workflows. Its unique workflow representation captures relationships between the processing steps, material and information flows, and data input and output. In this paper, we demonstrate the use of KProMS to manage and analyze the experimental data of an innovative system for manufacturing drug products using dropwise additive manufacturing. Dropwise additive manufacturing of pharmaceutical products (DAMPP) uses drop on demand printing technology for depositing various drug formulations onto edible substrates. DAMPP requires and generates a range of data types, including camera and IR images, spectra, and numerical parameter values, both of real-time and off-line natures, and thus provide a rich illustration of KProMS capabilities to serve as knowledge management framework.

Energy, and particularly electricity, has played and will continue to play a very important role in the development of human society. Electricity, which is the most flexible and manageable energy form, is currently used in a variety of activities and applications. For instance, electricity is used for heating, cooling, lighting, and for operating electronic appliances and electric vehicles. Nowadays, given the rapid development and commercialization of technologies and devices that rely on electricity, electricity demand is increasing faster than overall primary energy supply. Consequently, the design and planning of power systems is becoming a progressively more important issue in order to provide affordable, reliable and sustainable energy in timely fashion, not only in developed countries but particularly in developing economies where electricity demand is increasing even faster. Power systems are networks of electrical devices, such as power plants, transformers, and transmission lines, used to produce, transmit, and supply electricity. The design and planning of such systems require the selection of generation technologies, along with the capacity, location, and timing of generation and transmission capacity expansions to meet electricity demand over a long-term horizon. This manuscript presents a comprehensive optimization framework for the design and planning of interconnected power systems, including the integration of generation and transmission capacity expansion planning. The proposed framework also considers renewable energies, carbon capture and sequestration (CCS) technologies, demand-side management (DSM), as well as reserve and CO2 emission constraints. The novelty of this framework relies on an integrated assessment of the aforementioned features, which can reveal possible interactions and synergies within the power system. Moreover, the capabilities of the proposed framework are demonstrated using a suite of case studies inspired by a real-world power system, including “business as usual” and “CO2 mitigation policy” scenarios. These case studies illustrated the adaptability and effectiveness of the framework at dealing with typical situations that can arise in designing and planning power systems.

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.

The pharmaceutical industry has been undergoing a paradigm shift towards continuous manufacturing, under which novel approaches to real-time product quality assurance have been investigated. A new perspective, entitled Quality-by-Control (QbC), has recently been proposed as an important extension and complementary approach to enable comprehensive Quality-by-Design (QbD) implementation. In this study, a QbC approach was demonstrated for a commercial scale tablet press in a continuous direct compaction process. First, the necessary understanding of the compressibility of a model formulation was obtained under QbD guidance using a pilot scale tablet press, Natoli BLP-16. Second, a data reconciliation strategy was used to reconcile the tablet weight measurement based on this understanding on a commercial scale tablet press, Natoli NP-400. Parameter estimation to monitor and update the material property variance was also considered. Third, a hierarchical three-level control strategy, which addressed the fast process dynamics of the commercial scale tablet press was designed. The strategy consisted of the Level 0 built-in machine control, Level 1 decoupled Proportional Integral Derivative (PID) control loops for tablet weight, pre-compression force, main compression force, and production rate control, and Level 2 data reconciliation of sensor measurements. The effective and reliable performance, which could be demonstrated on the rotary tablet press, confirmed that a QbC approach, based on product and process knowledge and advanced model-based techniques, can ensure robustness and efficiency in pharmaceutical continuous manufacturing.

This manuscript assesses the accuracy and computational efficiency offered by three different strategies for the estimation of probability distributions, applied to DAE systems. Specifically, two approximate PDF estimation techniques, named ODMCMC and PDFE&U, are compared to Bayesian Markov-chain Monte Carlo (BMCMC), using a simulation-based approach. The results of our analysis show that ODMCMC and PDFE&U offer a good trade-off between accuracy and computational efficiency, thus are excellent choices for time-critical PDF estimation tasks.

In this work we develop a Bayesian framework for building surrogate stochastic models of complex multi-step processes for which tractable mechanistic models are difficult to construct. The probabilistic process envelope defined by the design space provides an extra level of assurance of product quality over and above that provided by traditional process control. While the application we report is specific to drug products manufactured using traditional batch processing, the proposed framework is applicable in general to batch and continuous manufacturing of products that must meet a set of critical product quality specifications.

In this work, a novel risk-based methodology for lot release is proposed. Its objective is to assess the risk that a lot declared to have passed truly meets product specifications. The methodology consists of 3 parts: adaptive sample size determination, estimation of the probability that the product was within specifications, and the lot-release decision. The methodology provides a probabilistic statement about the true quality of the batch. Having a probability estimate is the essential condition of risk-based decision-making. We demonstrate the proposed methodology on experimental data generated from 17 immediate-release solid oral drug products from a number of different manufacturers with 5 to 10 lots per manufacturer.

The shift from batch to continuous manufacturing, which is occurring in the pharmaceutical manufacturing industry has implications on process safety and product quality. It is now understood that fault-tolerant process control of critical process parameters (CPPs) and critical quality attributes (CQAs) is of paramount importance to the realization of safe operations and quality products. In this study, a systematic framework for fault-tolerant process control system design, analysis, and evaluation of pharmaceutical continuous oral solid dosage manufacturing is proposed. The framework encompasses system identification, controller design and analysis (controllability, stability, resilience, etc.), hierarchical three-level control structures (model predictive control, state estimation, data reconciliation, etc.), risk mapping, assessment and planning (Risk MAP) strategies, and control performance evaluation. The key idea of the proposed framework is to identify the potential risks associated with the control system design itself, the material property variations, and other process uncertainties, under which the control strategies must be evaluated. The framework is applied to a continuous direct compaction process, specifically the feeding-blending subsystem, wherein the major source of variance in the process operation and product quality arises. It is demonstrated, using simulations and experimentally, that the process operation failures and product quality variations in the feeding-blending system can be mitigated and managed through the proposed systematic fault-tolerant process control system design and risk analysis framework.

The principal method of drug delivery is by oral solid doses, the production of which often necessitates multiple post-crystallization unit operations to ensure content uniformity or enhance bioavailability. As an alternative to conventional dose production methods, applications of additive manufacturing technologies based on solvent- or melt-based formulations have demonstrated the potential for improvements to process efficiency, flexibility, and dosing precision. Here we explore the use of particulate suspensions in a dropwise additive manufacturing process as a method for dosing active ingredients in crystalline form, which may be difficult to achieve via powder processing due to poor flow properties. By employing a fluid-based method, powder flow issues are alleviated and adaptation of the process to new particles/crystals is facilitated by dimensional analysis. In this work, a feasibility study was conducted using 4 active ingredient powders, each with non-ideal particle properties, and 2 carrier fluids, in which the active ingredient does not dissolve, to formulate suspensions for dose manufacturing; drug products were analyzed to show reproducibility of dosing and to assess preservation of particle size through the process. Performance across particle types is affected by particle size and shape, and is related through effects on the rheological properties of the formulation.

Shale gas production has been the focus of intense debate in recent years. Shale gas supporters claim that it could be the way to transition between fossil fuels and renewable energy sources. For instance, in the United States, power generation from coal is being replaced by power generation from natural gas, which is a cleaner fuel when compared to coal. However, shale gas critics claim that the environmental cost associated with shale gas production is high enough to negate the benefits to society. For example, high water usage as well as the potential for contamination of underground and surface water sources constitute important environmental challenges for the development of shale gas resources. This study presents a methodology for the preliminary assessment of the development of shale gas resources taking into account well-pad design as one of the most important decision variables. To perform the assessment, different performance metrics are proposed to evaluate not only the economics of developing...

Control of particulate processes is hard to achieve because of the ease with which powders tend to segregate. Thus, proper sensing methods must be employed to ensure content uniformity during operation. The role of sensing schemes becomes even more critical while operating the process continuously as measurements are essential for implementation of feedback control (Austin et al. 2013. J Pharm Sci 102(6):1895-1904; Austin et al. 2014. Anal Chim Acta 819:82-93). A microwave sensor was developed and shown to be effective in online measurement of active pharmaceutical ingredient (API) concentration in a powder blend. During powder transport and hopper storage before processing, powder blends may segregate and cause quality deviations in the subsequent tableting operation. Therefore, it is critical to know the API concentration in the ribbons as the content uniformity is fixed once the ribbon is processed. In this study, a novel microwave sensor was developed that could provide measurement of a roller compacted ribbon's API concentration online, along with its density and moisture content. The results indicate that this microwave sensor is capable of increased accuracy compared with a commercially available near-IR probe for the determination of content uniformity and density in roller compacted ribbons online.

An inventory control system was developed for multiproduct batch plants with an arbitrary number of batch processes and storage units. Customer orders are received by the plant at order intervals and in order quantities that are subject to random fluctuations. The objective of the plant operation is to minimize the total cost while maintaining inventory levels within the storage or warehouse capacity by adjusting the startup times, the quantities of raw material orders, and production batch sizes. An adaptive model predictive control algorithm was developed that uses a periodic square wave model to represent the flows of the material between the processes and the storage units. The boundedness of the control output and the convergence of the estimated parameters in implementations of the proposed algorithm were mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of this approach was demonstrated by performing simulations. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1867–1880, 2015

This paper presents a real-time process management (RTPM) strategy for Dropwise Additive Manufacturing of Pharmaceutical Products (DAMPP), a mini-manufacturing method for pharmaceutical dosage forms. The semicontinuous, small-scale nature of DAMPP allows for more automation and control than traditional large-scale batch pharmaceutical manufacturing processes and can be used to manufacturing drug products with precise amounts of active pharmaceutical ingredients (API), suitable for production of high-potency drug products or individualized medicine. The RTPM strategy for DAMPP consists of advanced process control to ensure that every dosage unit meets quality specifications. We use temperature control systems and an imaging system linked to a LabVIEW automation program. The system is successful in controlling deposition of both solvent-based and melt-based dosage forms. It controls process and product temperature and monitors each drop visually. It records data pertinent to each deposited drop, determines the drop volume and thus API amount deposited, and automatically detects and diagnoses process faults. With a proper automation, control, and monitoring strategy, DAMPP is a viable manufacturing method for pharmaceutical dosage forms.