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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A new original method to reduce time and resource consuming photolithography mask production operations has been suggested. By means of femtosecond laser direct writing, thus, gaining cleaner surrounding than for nano- and pico-second pulse processing, a 45° angle ablation geometry has been proven. LED chip separation trenches and n -GaN layer exposure were made simultaneously saving from one fundamental processing and alignment step without adverse effect from the laser-processing on the quantum well region. [ABSTRACT FROM AUTHOR]

An inventory control system has been developed for a distribution system consisting of a single multiproduct warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process referred to as joint replenishment. The receipt of customer orders by the warehouse proceeds in order intervals and in order quantities that are subject to random fluctuations. The objective of warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm is developed using a periodic square wave model to represent the material flows. The adaptive concept incorporates a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The boundedness of the control output under the suggested algorithm is mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of the scheme was demonstrated using simulations. [ABSTRACT FROM AUTHOR]

Abstract: The term Process Systems Engineering (PSE) is relatively recent. It was coined about 50 years ago at the outset of the modern era of computer-aided engineering. However, the engineering of processing systems is almost as old as the beginning of the chemical industry, around the first half of the 19th century. Initially, the practice of PSE was qualitative and informal, but as time went on it was formalized in progressively increasing degrees. Today, it is solidly founded on engineering sciences and an array of systems-theoretical methodologies and computer-aided tools. This paper is not a review of the theoretical and methodological contributions by various researchers in the area of PSE. Its primary objective is to provide an overview of the history of PSE, i.e. its origin and evolution; a brief illustration of its tremendous impact in the development of modern chemical industry; its state at the turn of the 21st century; and an outline of the role it can play in addressing the societal problems that we face today such as; securing sustainable production of energy, chemicals and materials for the human wellbeing, alternative energy sources, and improving the quality of life and of our living environment. PSE has expanded significantly beyond its original scope, the continuous and batch chemical processes and their associated process engineering problems. Today, PSE activities encompass the creative design, operation, and control of: biological systems (prokaryotic and eukaryotic cells); complex networks of chemical reactions; free or guided self-assembly processes; micro- and nano-scale processes; and systems that integrate engineered processes with processes driven by humans, legal and regulatory institutions. Through its emphasis on synthesis problems, PSE provides the dialectic complement to the analytical bent of chemical engineering science, thus establishing the healthy tension between synthesis and analysis, the foundation of any thriving discipline. As a consequence, throughout this paper PSE emerges as the foundational underpinning of modern chemical engineering; the one that ensures the discipline''s cohesiveness in the years to come. [Copyright &y& Elsevier]

Abstract: A supply chain (SC) model incorporating business strategic decision components is an important tool for gaining a competitive edge in todays global market. Enterprise models of this type must encompass not only the SC, but also the demand chain since understanding the market is crucial for developing good business policies. To operate effectively, marketing activities must be coordinated with other corporate functional areas. Specifically, managers should evaluate the trade-off between marketing and SC decisions to enhance the performance of the overall metric: the shareholders value. Recently, there has been significant progress in developing marketing science models for reaching quantitatively based marketing decisions. In this work, we build on these developments to formulate a mathematical model that accounts for the main relevant business functionalities. The result is a MINLP formulation which optimizes the SC and marketing strategic decisions in an integrated fashion. Moreover, a financial model that evaluates the enterprise value is also incorporated. [ABSTRACT FROM AUTHOR]

• Image processing, computational geometry and Bayesian modeling enable robust monitoring. • Incorporation of physical understanding yields semi-mechanistic hybrid models. • Uncertainty quantification at in-/out-of-sample conditions via hierarchical regression. • Real-time, risk-based control strategy for additive manufacturing of pharmaceuticals. With the increasing availability of real-time process data, the Bayesian framework is an adaptable means for dealing with complex models and data structures which can provide readily interpretable outputs for first-principles and hybrid models. Applied to process monitoring and control scenarios, Bayesian models have the capability to provide informed decisions about quantities of interest derived from the online process measurements through the use of probability as a measure of uncertainty. This work presents one such application, wherein real-time image data is used to predict the content of pharmaceutical dose units produced by an additive manufacturing process; a probabilistic quality control strategy is therefore enacted at the level of each dosage unit. Utilizing a hierarchical modeling approach, we are able to draw upon a large historical database to model sources of uncertainty across a wide range of conditions, thus yielding more reliable predictions for future data at in-sample and out-of-sample conditions. [ABSTRACT FROM AUTHOR]

The process systems engineering (PSE) as well as the operations research and management science (ORMS) literature has hitherto focused on disparate processes and functions within the enterprise. These themes have included upstream R&D pipeline management, planning and scheduling in batch and continuous manufacturing systems and more recently supply chain optimization under uncertainty. In reality, the modern process enterprise functions as a cohesive entity involving several degrees of cross-functional co-ordination across enterprise planning and process functions. The complex organizational structures underlying horizontally and vertically integrated process enterprises challenge our understanding of cross-functional co-ordination and its business impact. This article looks at the impact of enterprise-wide cross-functional coordination on enterprise performance, sustainability and growth prospects. Cross-functional coordination is defined as the integration of strategic and tactical decision-making processes involving the control of financial and inventory flows (both internal and external) as well as resource deployments. Initially, we demonstrate the existence of cross-functional decision-making dependencies using an enterprise network model. Subsequently, we discuss interactions between enterprise planning decisions involving project financing, debt-equity balancing, R&D portfolio selection, risk hedging with real derivative instruments, supply chain asset creation and marketing contracts which influence decision-making at the activity/process level. Several case studies are included to re-enforce the point that planning and process decisions need to be integrated. [Copyright &y& Elsevier]

Abstract: The aqueous sol–gel synthesis technique for the preparation of YBa2Cu4O8 (Y-124) superconductor was studied using two different complexing agents with the same chemical composition, namely l-(+)-tartaric (natural) and dl-tartaric (synthetic) acids. The characterization of Y–Ba–Cu–O gels by XRD and TGA/DTA measurements showed the individuality of precursor gels and a high level of homogeneity. On the other hand, the morphological features of the gels observed by SEM measurements were slightly different. The ceramic samples calcined for 30h at 780°C in a flowing oxygen atmosphere contained homogeneous Y-124 crystallites as a major phase, as was shown by XRD analysis. The T C (onset) observed by resistivity and magnetic susceptibility measurements was approximately the same for the both samples (78–80K). However, the temperature–resistivity and temperature–magnetic susceptibility dependences were found to be different for the Y-124 samples synthesized by different sol–gel procedures. Such behaviour was discussed in correlation with the morphological properties of the prepared samples. [Copyright &y& Elsevier]

Abstract: By Monte Carlo simulations, we investigate the current voltage characteristic and the associated current noise in GaN double-drift impact avalanche diodes. For values of the current multiplication factor greater than ten we find a giant suppression of avalanche noise down to three order of magnitude with respect to the standard excess noise factor. The negative feedback between fluctuations in space charge and in number of generated electron–hole pairs is proven to be the responsible of such a giant suppression. [Copyright &y& Elsevier]

The simultaneous strategy and control parameterization are the two most widely used direct methods for solving dynamic optimization problems. The control parameterization approach generally results in a comparatively smaller nonlinear program (NLP) but has difficulties in dealing with the path constraints. The advantage of the simultaneous approach lies in its ability to handle path constraints, thus eliminating the need to obtain expensive and possibly infeasible intermediate solutions. The disadvantage is that it requires solution of a potentially very large dimensional NLP. This work presents a decomposition strategy, which combines the advantages of the control parameterization and simultaneous approaches for solving dynamic optimization problems with path constraints. Using the proposed strategy, first the set of state variables x is divided into two sets x1 and x2, and the system is partitioned into two corresponding sub-systems. The criterion used to partition the state variables and the system model are that the equations which define the state variables involved in the path constraints should be in the same sub-system. For the resulting sub-systems, one is enforced in the master NLP through collocation method as in simultaneous approach, the other is solved together with the sensitivities in a differential and algebraic equations (DAE) solver. This strategy constitutes a general approach in the sense that for problems with a specific structure the method is equivalent to the control parameterization method, while for other problems with special structures the approach is the same as the simultaneous approach. In general it possesses the advantage of the simultaneous method in handling the path constraints since it is directly enforced in the master NLP as well as the advantage of control parameterization in resulting a small master NLP because only a fraction of the state variables are directly discretized. The method is demonstrated through several numerical examples. [Copyright &y& Elsevier]

The pharmaceutical industry has traditionally relied on mass manufacturing to make its products. This has created multiple problems in the drug supply network, including long production times, inflexible and sluggish manufacturing and lack of personalized dosing. The industry is gradually adapting to these challenges and is developing novel technologies to address them. Continuous manufacturing and 3D printing are two promising techniques that can revolutionize pharmaceutical manufacturing. However, most research studies into these methods tend to treat them separately. This study seeks to develop a new processing route to continuously integrate a 3D printing platform (Drop-on-Demand, DoD, printing) with crystallization that is generally the final step of the active ingredient manufacturing. Accomplishing this integration would enable harnessing the benefits of each method- personalized dosing of 3D printing and flexibility and speed of continuous manufacturing. A novel unit operation, three-phase settling (TPS), is developed to integrate DoD with the upstream crystallizer. To ensure on-spec production of each printed dosage, two process analytical technology tools are incorporated in the printer to monitor drug loading in manufactured drug products in real time. Experimental demonstration of this system is carried out via two case studies: the first study uses an active ingredient celecoxib to test the standalone operation of TPS; the second study demonstrates the operation of the integrated system (crystallizer – TPS – DoD) to continuously make drug products for the active ingredient- lomustine. A dissolution test is also performed on the manufactured and commercial lomustine drug products to compare their dissolution behavior. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry has shown keen interest in developing small-scale modular manufacturing systems for producing medicinal products. These systems offer agile and flexible manufacturing, and are well-suited for use in situations requiring rapid production of drugs such as pandemics and humanitarian disasters. The creation of such systems requires the development of modular facilities for making solid oral drug products. In recent years, however, the development of such facilities has seen limited progress. This study presents a development of a prototype modular system that uses drop on demand (DoD) printing to produce personalized solid oral drug products. The system's operation is demonstrated for manufacturing mini-tablets, a category of pediatric drug products, in continuous and semi-batch modes. In this process, the DoD printer is used to generate molten formulation drops that are solidified into mini-tablets. These dosages are then extracted, washed and dried in a continuous filtration and drying unit which is integrated with the printer. Process monitoring tools are also incorporated in the system to track the critical quality attributes of the product and the critical process parameters of the manufacturing operation in real time. Future areas of innovation are also proposed to improve this prototype unit and to enable the development of advanced drug manufacturing systems based on this platform. [ABSTRACT FROM AUTHOR]

Highlights • Climate change impacts on a hydro-dominated power system are evaluated. • Key uncertain parameters are identified using global sensitivity analysis. • A two-stage stochastic approach for power system design and planning is developed. • Climate change will reduce the capacity factor of hydropower plants in Colombia. • Climate change will increase natural gas demand in the Colombian power sector. Abstract 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. [ABSTRACT FROM AUTHOR]

This contribution presents a framework for addressing the campaign scheduling, dynamic optimization and optimal control of batch processes in an integrated fashion. The strategy is comprised of an offline and an online phase. The first involves solving a conventional campaign scheduling problem and serves to generate key information needed in the second. The latter consists of a modified dynamic optimization/optimal control algorithm and serves to update the offline campaign schedule in real time as well as to provide the batch process with optimal control actions to achieve maximum campaign profit/performance. As a result of this two-phase architecture, the algorithm avoids the solution of a mixed-integer optimization problem online and can support virtually any process recipe structure including any type of recycle. To demonstrate its potential, we test the proposed methodology to solve the integrated campaign scheduling, dynamic optimization and optimal control of a batch plant for the production of nopol. [ABSTRACT FROM AUTHOR]

Current technologies to measure granule flowability involve at-line methods that can take hours to perform. This is problematic for a continuous dry granulation tableting line, where the quality assurance and control of the final tablet products depend on real-time monitoring and control of powder flowability. Hence, a real-time alternative is needed for measuring the flowability of the granular products coming out of the roller compactor, which is the unit operation immediately preceding the tablet press. Since particle analyzers have the potential to take inline measurements of the size and shape of granules, they can potentially serve as real-time flowability sensors, given that the size and shape measurements can be used to reliably predict flowability measurements. This paper reports on the use of Partial Least Squares (PLS) regression to utilize distributions of size and shape measurements in predicting the output of three different types of flowability measurements: rotary drum flow, orifice flow, and tapped density analysis. The prediction performance of PLS had a coefficient of determination ranging from 0.80 to 0.97, which is the best reported performance in the literature. This is attributed to the ability of PLS to handle high collinearity in the datasets and the inclusion of multiple shape characteristics—eccentricity, form factor, and elliptical form factor—into the model. The latter calls for a change in industry perspective, which normally dismisses the importance of shape in favor of size; and the former suggests the use of PLS as a better way to reduce the dimensionality of distribution datasets, instead of the widely used practice of pre-selecting distribution percentiles. [ABSTRACT FROM AUTHOR]

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 CO 2 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 “CO 2 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. [ABSTRACT FROM AUTHOR]

The paper presents a dropwise additive manufacturing process for pharmaceutical products (DAMPP) as an alternative to conventional methods. This mini manufacturing process for the production of personalized pharmaceutical products utilizes drop-on-demand (DoD) printing technology for the deposition of active pharmaceutical ingredient (API) onto edible substrates. Here we present a process control framework for DAMPP, including on-line monitoring, automation and closed loop control, in order to produce individual dosage forms with the desired critical quality attributes, including formulation composition, drop size, deposit morphology and dissolution performance. In order to achieve desired product morphology, a surrogate model based on polynomial chaos expansion is developed to relate the critical process parameters to deposit morphology using dissolution data of the active pharmaceutical ingredient. The proposed process control strategy can effectively mitigate variations in the dissolution profiles due to variable dosage amounts and enable the application of the DoD system for the production of individualized dosage regimens. [ABSTRACT FROM AUTHOR]