In situ NIR spectroscopy monitoring of plasmid production processes effect of producing strain, medium composition and the cultivation strategy

BACKGROUNDWhile the pharmaceutical industry keeps an eye on plasmid DNA production for new generation gene therapies, real-time monitoring techniques for plasmid bioproduction are as yet unavailable. This work shows the possibility of in situ monitoring of plasmid production in Escherichia coli cultures using a near infrared (NIR) fiber optic probe. RESULTSPartial least squares (PLS) regression models based on the NIR spectra were developed for predicting bioprocess critical variables such as the concentrations of biomass, plasmid, carbon sources (glucose and glycerol) and acetate. In order to achieve robust models able to predict the performance of plasmid production processes, independently of the composition of the cultivation medium, cultivation strategy (batch versus fed-batch) and E. coli strain used, three strategies were adopted, using: (i) E. coliDH5 cultures conducted under different media compositions and culture strategies (batch and fed-batch); (ii) engineered E. coli strains, MG1655endArecApgi and MG1655endArecA, grown on the same medium and culture strategy; (iii) diverse E. coli strains, over batch and fed-batch cultivations and using different media compositions. PLS models showed high accuracy for predicting all variables in the three groups of cultures. CONCLUSIONNIR spectroscopy combined with PLS modeling provides a fast, inexpensive and contamination-free technique to accurately monitoring plasmid bioprocesses in real time, independently of the medium composition, cultivation strategy and the E. coli strain used.

Assessing environmental impacts using a comparative LCA of industrial and artisanal production processes: "Minas Cheese" case

This study uses the Life Cycle Assessment (LCA) methodology to evaluate and compare the environmental impacts caused by both the artisanal and the industrial manufacturing processes of "Minas cheese". This is a traditional cheese produced in the state of Minas Gerais (Brazil), and it is considered a "cultural patrimony" in the country. The high participation of artisanal producers in the market justifies this research, and this analysis can help the identification of opportunities to improve the environmental performance of several stages of the production system. The environmental impacts caused were also assessed and compared. The functional unit adopted was 1 kilogram (Kg) of cheese. The system boundaries considered were the production process, conservation of product (before sale), and transport to consumer market. The milk production process was considered similar in both cases, and therefore it was not included in the assessment. The data were collected through interviews with the producers, observation, and a literature review; they were ordered and processed using the SimaPro 7 LCA software. According to the impact categories analyzed, the artisanal production exerted lower environmental impacts. This can be justified mainly because the industrial process includes the pasteurization stage, which uses dry wood as an energy source and refrigeration.

Utilisation of by-products from sunflower-based biodiesel production processes for the production of fermentation feedstock

By-products streams from a sunflower-based biodiesel plant were utilised for the production of fermentation media that can be used for the production of polyhydroxyalkanoates (PHA). Sunflower meal was utilised as substrate for the production of crude enzyme consortia through solid state fermentation (SSF) with the fungal strain Aspergillus oryzae. Fermented solids were subsequently mixed with unprocessed sunflower meal aiming at the production of a nutrient-rich fermentation feedstock. The highest free amino nitrogen (FAN) and inorganic phosphorus concentrations achieved were 1.5 g L-1 and 246 mg L-1, respectively, when an initial proteolytic activity of 6.4 U mL-1 was used. The FANconcentrationwas increased to 2.3 g L-1 when the initial proteolytic activity was increased to 16 U mL-1. Sunflower meal hydrolysates were mixed with crude glycerol to provide fermentationmedia that were evaluated for the production of poly(3-hydroxybutyrateco- 3-hydroxyvalerate) (P(3HB-co-3HV)) using Cupriavidus necator DSM545. The P(3HB-co-3HV) (9.9 g l-1) produced contained 3HB and 3HV units with 97 and 3 mol %, respectively. Integrating PHA production in existing 1st generation biodiesel production plants through valorisation of by-product streams could improve their sustainability.

Clavulanic acid production processes in a tower bioreactor with immobilised cells

The feasibility of using Streptomyces clavuligerus ATCC 27064 bioparticles supported on alginate gel containing alumina to produce clavulanic acid (CA) was investigated. To this end, effectiveness factors for spherical bioparticles, relating respiration rates of immobilised and free cells, were experimentally determined for various dissolved oxygen (DO) levels and bioparticle radii. Monod kinetics was assumed as representative of the oxygen consuming reaction, while internal oxygen diffusion was considered the limiting step. A comparison was made of the results from a tower bioreactor operating under batch, repeated-batch and continuous conditions with immobilised bioparticles. The theoretical curve of the effectiveness factor for the zero-order reaction model, considering an inert nucleus - the dead core model - was very well fitted to the experimental data. The results of the bioprocess indicated that the batch operation was the most efficient and productive, requiring a do concentration in the reactor above 60% of the saturation value. (C) 2007 Elsevier B.V. All rights reserved.

Exergy efficiency analysis of chemical and biochemical stages involved in liquid biofuels production processes

Liquid biofuels can be produced from a variety of feedstocks and processes. Ethanol and biodiesel production processes based on conventional raw materials are already commercial, but subject to further improvement and optimization. Biofuels production processes using lignocellulosic feedstocks are still in the demonstration phase and require further R&D to increase efficiency. A primary tool to analyze the efficiency of biofuels production processes from an integrated point of view is offered by exergy analysis. To gain further insight into the performance of biofuels production processes, a simulation tool, which allows analyzing the effect of process variables on the exergy efficiency of stages in which chemical or biochemical reactions take place, were implemented. Feedstocks selected for analysis were parts or products of tropical plants such as the fruit and flower stalk of banana tree, palm oil, and glucose syrups. Results of process simulation, taking into account actual process conditions, showed that the exergy efficiencies of the acid hydrolysis of banana fruit and banana pulp were in the same order (between 50% and 60%), lower than the figure for palm oil transesterification (90%), and higher that the exergy efficiency of the enzymatic hydrolysis of flower stalk (20.3%). (C) 2011 Elsevier Ltd. All rights reserved.

High precision tools for slepton pair production processes at hadron colliders

In this thesis, we develop high precision tools for the simulation of slepton pair production processes at hadron colliders and apply them to phenomenological studies at the LHC. Our approach is based on the POWHEG method for the matching of next-to-leading order results in perturbation theory to parton showers. We calculate matrix elements for slepton pair production and for the production of a slepton pair in association with a jet perturbatively at next-to-leading order in supersymmetric quantum chromodynamics. Both processes are subsequently implemented in the POWHEG BOX, a publicly available software tool that contains general parts of the POWHEG matching scheme. We investigate phenomenological consequences of our calculations in several setups that respect experimental exclusion limits for supersymmetric particles and provide precise predictions for slepton signatures at the LHC. The inclusion of QCD emissions in the partonic matrix elements allows for an accurate description of hard jets. Interfacing our codes to the multi-purpose Monte-Carlo event generator PYTHIA, we simulate parton showers and slepton decays in fully exclusive events. Advanced kinematical variables and specific search strategies are examined as means for slepton discovery in experimentally challenging setups.

Knowledge-Production Processes in Practice Research - Outcomes and Critical Elements

The welfare sector has seen considerable changes in its operational context. Welfare services respond to an increasing number of challenges as citizens are confronted with life’s uncertainties and a variety of complex situations. At the same time the service-delivery system is facing problems of co-operation and the development of staff competence, as well as demands to improve service effectiveness and outcomes. In order to ensure optimal user outcomes in this complex, evolving environment it is necessary to enhance professional knowledge and skills, and to increase efforts to develop the services. Changes are also evident in the new emergent knowledge-production models. There has been a shift from knowledge acquisition and transmission to its construction and production. New actors have stepped in and the roles of researchers are subject to critical discussion. Research outcomes, in other words the usefulness of research with respect to practice development, is a topical agenda item. Research is needed, but if it is to be useful it needs to be not only credible but also useful in action. What do we know about different research processes in practice? What conceptions, approaches, methods and actor roles are embedded? What is the effect on practice? How does ‘here and now’ practice challenge research methods? This article is based on the research processes conducted in the institutes of practice research in social work in Finland. It analyses the different approaches applied by elucidating the theoretical standpoints and the critical elements embedded in them, and reflects on the outcomes in and for practice. It highlights the level of change and progression in practice research, arguing for diverse practice research models with a solid theoretical grounding, rigorous research processes, and a supportive infrastructure.

A continuous-time MILP model for short-term scheduling of make-and-pack production processes

In process industries, make-and-pack production is used to produce food and beverages, chemicals, and metal products, among others. This type of production process allows the fabrication of a wide range of products in relatively small amounts using the same equipment. In this article, we consider a real-world production process (cf. Honkomp et al. 2000. The curse of reality – why process scheduling optimization problems are diffcult in practice. Computers & Chemical Engineering, 24, 323–328.) comprising sequence-dependent changeover times, multipurpose storage units with limited capacities, quarantine times, batch splitting, partial equipment connectivity, and transfer times. The planning problem consists of computing a production schedule such that a given demand of packed products is fulfilled, all technological constraints are satisfied, and the production makespan is minimised. None of the models in the literature covers all of the technological constraints that occur in such make-and-pack production processes. To close this gap, we develop an efficient mixed-integer linear programming model that is based on a continuous time domain and general-precedence variables. We propose novel types of symmetry-breaking constraints and a preprocessing procedure to improve the model performance. In an experimental analysis, we show that small- and moderate-sized instances can be solved to optimality within short CPU times.

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

Due to the ongoing trend towards increased product variety, fast-moving consumer goods such as food and beverages, pharmaceuticals, and chemicals are typically manufactured through so-called make-and-pack processes. These processes consist of a make stage, a pack stage, and intermediate storage facilities that decouple these two stages. In operations scheduling, complex technological constraints must be considered, e.g., non-identical parallel processing units, sequence-dependent changeovers, batch splitting, no-wait restrictions, material transfer times, minimum storage times, and finite storage capacity. The short-term scheduling problem is to compute a production schedule such that a given demand for products is fulfilled, all technological constraints are met, and the production makespan is minimised. A production schedule typically comprises 500–1500 operations. Due to the problem size and complexity of the technological constraints, the performance of known mixed-integer linear programming (MILP) formulations and heuristic approaches is often insufficient. We present a hybrid method consisting of three phases. First, the set of operations is divided into several subsets. Second, these subsets are iteratively scheduled using a generic and flexible MILP formulation. Third, a novel critical path-based improvement procedure is applied to the resulting schedule. We develop several strategies for the integration of the MILP model into this heuristic framework. Using these strategies, high-quality feasible solutions to large-scale instances can be obtained within reasonable CPU times using standard optimisation software. We have applied the proposed hybrid method to a set of industrial problem instances and found that the method outperforms state-of-the-art methods.

Utilizing the Internet of Things to promote energy awareness and efficiency at discrete production processes: Practices and methodology

El actual contexto de fabricación, con incrementos en los precios de la energía, una creciente preocupación medioambiental y cambios continuos en los comportamientos de los consumidores, fomenta que los responsables prioricen la fabricación respetuosa con el medioambiente. El paradigma del Internet de las Cosas (IoT) promete incrementar la visibilidad y la atención prestada al consumo de energía gracias tanto a sensores como a medidores inteligentes en los niveles de máquina y de línea de producción. En consecuencia es posible y sencillo obtener datos de consumo de energía en tiempo real proveniente de los procesos de fabricación, pero además es posible analizarlos para incrementar su importancia en la toma de decisiones. Esta tesis pretende investigar cómo utilizar la adopción del Internet de las Cosas en el nivel de planta de producción, en procesos discretos, para incrementar la capacidad de uso de la información proveniente tanto de la energía como de la eficiencia energética. Para alcanzar este objetivo general, la investigación se ha dividido en cuatro sub-objetivos y la misma se ha desarrollado a lo largo de cuatro fases principales (en adelante estudios). El primer estudio de esta tesis, que se apoya sobre una revisión bibliográfica comprehensiva y sobre las aportaciones de expertos, define prácticas de gestión de la producción que son energéticamente eficientes y que se apoyan de un modo preeminente en la tecnología IoT. Este primer estudio también detalla los beneficios esperables al adoptar estas prácticas de gestión. Además, propugna un marco de referencia para permitir la integración de los datos que sobre el consumo energético se obtienen en el marco de las plataformas y sistemas de información de la compañía. Esto se lleva a cabo con el objetivo último de remarcar cómo estos datos pueden ser utilizados para apalancar decisiones en los niveles de procesos tanto tácticos como operativos. Segundo, considerando los precios de la energía como variables en el mercado intradiario y la disponibilidad de información detallada sobre el estado de las máquinas desde el punto de vista de consumo energético, el segundo estudio propone un modelo matemático para minimizar los costes del consumo de energía para la programación de asignaciones de una única máquina que deba atender a varios procesos de producción. Este modelo permite la toma de decisiones en el nivel de máquina para determinar los instantes de lanzamiento de cada trabajo de producción, los tiempos muertos, cuándo la máquina debe ser puesta en un estado de apagada, el momento adecuado para rearrancar, y para pararse, etc. Así, este modelo habilita al responsable de producción de implementar el esquema de producción menos costoso para cada turno de producción. En el tercer estudio esta investigación proporciona una metodología para ayudar a los responsables a implementar IoT en el nivel de los sistemas productivos. Se incluye un análisis del estado en que se encuentran los sistemas de gestión de energía y de producción en la factoría, así como también se proporcionan recomendaciones sobre procedimientos para implementar IoT para capturar y analizar los datos de consumo. Esta metodología ha sido validada en un estudio piloto, donde algunos indicadores clave de rendimiento (KPIs) han sido empleados para determinar la eficiencia energética. En el cuarto estudio el objetivo es introducir una vía para obtener visibilidad y relevancia a diferentes niveles de la energía consumida en los procesos de producción. El método propuesto permite que las factorías con procesos de producción discretos puedan determinar la energía consumida, el CO2 emitido o el coste de la energía consumida ya sea en cualquiera de los niveles: operación, producto o la orden de fabricación completa, siempre considerando las diferentes fuentes de energía y las fluctuaciones en los precios de la misma. Los resultados muestran que decisiones y prácticas de gestión para conseguir sistemas de producción energéticamente eficientes son posibles en virtud del Internet de las Cosas. También, con los resultados de esta tesis los responsables de la gestión energética en las compañías pueden plantearse una aproximación a la utilización del IoT desde un punto de vista de la obtención de beneficios, abordando aquellas prácticas de gestión energética que se encuentran más próximas al nivel de madurez de la factoría, a sus objetivos, al tipo de producción que desarrolla, etc. Así mismo esta tesis muestra que es posible obtener reducciones significativas de coste simplemente evitando los períodos de pico diario en el precio de la misma. Además la tesis permite identificar cómo el nivel de monitorización del consumo energético (es decir al nivel de máquina), el intervalo temporal, y el nivel del análisis de los datos son factores determinantes a la hora de localizar oportunidades para mejorar la eficiencia energética. Adicionalmente, la integración de datos de consumo energético en tiempo real con datos de producción (cuando existen altos niveles de estandarización en los procesos productivos y sus datos) es esencial para permitir que las factorías detallen la energía efectivamente consumida, su coste y CO2 emitido durante la producción de un producto o componente. Esto permite obtener una valiosa información a los gestores en el nivel decisor de la factoría así como a los consumidores y reguladores. ABSTRACT In today‘s manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision makers to prioritize green manufacturing. The Internet of Things (IoT) paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from the manufacturing processes can be easily collected and then analyzed, to improve energy-aware decision-making. This thesis aims to investigate how to utilize the adoption of the Internet of Things at shop floor level to increase energy–awareness and the energy efficiency of discrete production processes. In order to achieve the main research goal, the research is divided into four sub-objectives, and is accomplished during four main phases (i.e., studies). In the first study, by relying on a comprehensive literature review and on experts‘ insights, the thesis defines energy-efficient production management practices that are enhanced and enabled by IoT technology. The first study also explains the benefits that can be obtained by adopting such management practices. Furthermore, it presents a framework to support the integration of gathered energy data into a company‘s information technology tools and platforms, which is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage such data in order to improve energy efficiency. Considering the variable energy prices in one day, along with the availability of detailed machine status energy data, the second study proposes a mathematical model to minimize energy consumption costs for single machine production scheduling during production processes. This model works by making decisions at the machine level to determine the launch times for job processing, idle time, when the machine must be shut down, ―turning on‖ time, and ―turning off‖ time. This model enables the operations manager to implement the least expensive production schedule during a production shift. In the third study, the research provides a methodology to help managers implement the IoT at the production system level; it includes an analysis of current energy management and production systems at the factory, and recommends procedures for implementing the IoT to collect and analyze energy data. The methodology has been validated by a pilot study, where energy KPIs have been used to evaluate energy efficiency. In the fourth study, the goal is to introduce a way to achieve multi-level awareness of the energy consumed during production processes. The proposed method enables discrete factories to specify energy consumption, CO2 emissions, and the cost of the energy consumed at operation, production and order levels, while considering energy sources and fluctuations in energy prices. The results show that energy-efficient production management practices and decisions can be enhanced and enabled by the IoT. With the outcomes of the thesis, energy managers can approach the IoT adoption in a benefit-driven way, by addressing energy management practices that are close to the maturity level of the factory, target, production type, etc. The thesis also shows that significant reductions in energy costs can be achieved by avoiding high-energy price periods in a day. Furthermore, the thesis determines the level of monitoring energy consumption (i.e., machine level), the interval time, and the level of energy data analysis, which are all important factors involved in finding opportunities to improve energy efficiency. Eventually, integrating real-time energy data with production data (when there are high levels of production process standardization data) is essential to enable factories to specify the amount and cost of energy consumed, as well as the CO2 emitted while producing a product, providing valuable information to decision makers at the factory level as well as to consumers and regulators.

Trace metals and marine production processes : proceedings of a workshop held at the Pacific Marine Environmental Laboratory, February 28 -March 2, 1978 /

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