Today we offer unique product solutions that can be used to automate many production processes. These products and companies are considered by most to be the industry standard for quality, innovation, and value within their type of equipment.
About Schneider Electric
Schneider Electric is the global specialist in energy management and automation. With revenues of ~$30 billion in FY2015, our 160,000+ employees serve customers in over 100 countries, helping them to manage their energy and process in ways that are safe, reliable, efficient and sustainable. From the simplest of switches to complex operational systems, our technology, software and services improve the way our customers manage and automate their operations. Our connected technologies reshape industries, transform cities and enrich lives. At Schneider Electric, we call this Life Is On.
Automate Schneider Tsx Pdf Free
Download 🔥 https://ssurll.com/2xYipG 🔥
In offshore and onshore wells in the Middle East, Africa, and Latin America, the EcoStruxure Autonomous Production Advisor model training process has proven to be very effective at capturing the skills and expertise of the most senior operators and having the system automate and reproduce them. In one use case run by Schneider Electric, the customer reported a 13% increase in production and a 33% reduction in energy consumption.
'Automation of science' bears the promise of making better decisions faster1. In drug discovery, automated systems already have a long and fruitful history2 (Fig. 1). Medium-throughput to high-throughput robotic screening in specialized assays has become standard in the pharmaceutical industry (Fig. 2). The breadth of other applications of automated systems extends from decision-support systems, to computational molecular design to fully fledged robotic synthesis and hit finding3. Prominent examples include traditional rule-based and model-based approaches (for example, the archetypal DENDRAL system for analysing mass spectra4, LHASA5 software for synthesis planning and various in-house tools for accessing and analysing chemical and biological data similar to Amgen's AADAPT system6), various software tools for de novo molecular design7 and prototypical robotic systems such as ADAM and EVE for automated target and hit finding1,8.
The automation and parallelization of chemical synthesis offer benefits such as increased speed and throughput, greater reproducibility, lower consumption of materials and, consequently, the possibility to explore wider areas of chemical space within a given time frame compared with manual, serial compound synthesis57. Historically, the first automated synthetic processes and robots were conceived for peptides58,59 (Merrifield's method for amide bond formation), oligonucleotides60,61 (solid-phase phosphoramidite method for internucleotide linkage) and later for oligosaccharides62 (for example, the trichloroacetimidate method for glycosidic bond formation).
A key element in each of these processes is the use of a small set of building blocks (including larger fragments) and a well-defined, robust chemical reaction to afford large sets of diverse products in high yields by iterative building block assembly, orthogonal protection group chemistry and purification. Various methodological and technical improvements, including stereoselective synthesis, parallelization of subprocesses and preparatory steps, miniaturization (small volumes and compact synthesis arrays) and automated in-line purification, have resulted in highly reliable synthesis machines for increasingly complex oligomeric structures. Their underlying general design concept mimics the biosynthesis of most natural products. Furthermore, combinatorial thinking has led to methods for the massively parallelized scaffold-centric synthesis of structurally diverse compound libraries63. Many of these approaches are readily amenable to miniaturization and inclusion in automated design cycles64. Researchers at Eli Lilly have established a superb example of such a fully automated robotic synthesis laboratory that can be remotely controlled, which is a major step towards advancing the efficiency and effectiveness of chemical synthesis for drug discovery65,66.
The example demonstrates the concept of sequential boronate building block assembly. Four building blocks (coloured circles) are combined in a standardized deprotection, coupling and purification process. Synthesizers implementing this and other combinatorial reaction schemes can serve as chemistry modules in automated drug discovery platforms. Adapted with permission from Ref. 74, Science/AAAS.
Nevertheless, there are limitations to continuous-flow systems including the (in)stability of the fluidic interfaces between microscopic and macroscopic fluid handling and the deposition of reactive by-products, and automated batch synthesis and fast parallel synthetic strategies have been suggested as alternatives112. For example, researchers at Merck recently presented their 'chemical high-throughput experimentation' (HTE) platform in 3,456-well microtitre plates, aiming to optimize a key synthetic step in a drug discovery programme. HTE successfully identified the preferred catalyst, reaction conditions, reagents and solvents for the given transformation. The authors conclude that hypothesis-driven HTE allows a scientist to 'go fast' and may be considered the logical extension of traditional chemical experimentation113. Chow and Nelson114 have argued that automated HTE discovery workflows may enable expansion of the synthetic chemistry toolkit and increase innovation in medicinal chemistry.
Coupling the individual components is an engineering challenge. The majority of platforms currently being introduced in industry for the automated parallel synthesis of small, focused compound libraries seem to operate without making extensive use of microfluidics-assisted chemical synthesis, probably because for certain microfluidic reactors, clogging of the reactor channels and leakage due to back-pressure issues or incompatibility of the solvents and materials remain a major problem. Performing chemical flow reactions in droplet environments offers a potential solution to several of these problems. Droplets may be considered isolated mini-reactors with volumes reduced to the femtolitre scale120,121, facilitating sorting and process control122. DeMello and co-workers123,124 have demonstrated that droplet-based microfluidics systems are precise tools for studying and optimizing the synthetic parameters of chemical reactions, leading to the production of materials with superior characteristics (Fig. 5).
Advanced nanotechnology offers even farther-reaching opportunities such as micromachines (nanobots) for drug delivery137. In fact, the prospect of combining nanotechnological devices with on-chip testing of computationally designed compounds does not seem far-fetched. Advances in chemical imaging further augment the capabilities of on-chip monitoring, for example, by miniature electrode arrays for high-resolution peak analysis138. 'Plug-and-play' microfluidics modules are the next step towards fully integrated on-chip drug discovery. Miled and co-workers developed such a modular lab-on-a-chip device for automated monitoring and modulating of the concentrations of neurotransmitters such as dopamine and serotonin, thereby opening new possibilities for functional drug screening with feedback control139.
The classic linear layout shown in part a does not contain automated feedback from the assay to the reagent selection, whereas the cyclic layout shown in part b includes an adaptive computer model for reactant prioritization based on the assay readout. LC, liquid chromatography; MS, mass spectrometry; UV, ultraviolet light.
Current computational tools are largely data driven. For example, ReactionExplorer is based on thousands of manually curated rules (electron-transfer steps) that represent basic chemical transformations to devise a mechanistic interpretation of a plausible reaction pathway156. More recently, machine learning models have been developed for automated synthesis planning, enabled by large curated reaction databases. ReactionPredictor is such a method and automatically identifies and ranks electron-transfer steps by use of a simplified molecular orbital description157. The number of prospective applications of these and other tools is still limited, and there is not much experience, if any, with integrating such tools in automated synthesis platforms. However, the continuously growing 'Network of Organic Chemistry' (NOC) contains approximately ten million reactions and reactants for synthesis planning158. One may consider such a collection of facts 'big data' in chemistry. Szymkuc et al.159 presented an innovative approach to reaction pathway construction based on NOC, using fast graph-analysis methods borrowed from bioinformatics. These algorithms are able to efficiently navigate through the entire breadth of chemical synthesis knowledge to identify optimal synthetic pathways. Alternative synthetic routes leading from the reactants to the products are compared using a function that includes the number of steps and the cost of synthesis. Finally, algorithmically identified optimal syntheses are obtained.
Artificial intelligence in molecular design. Aside from the required robotic hardware and synthesize-and-test machinery, the learning aspect probably represents the most crucial part of the automated design cycle. If the design hypothesis is wrong, then even the most advanced synthesize-and-test approach will fail to deliver, irrespective of the technology used. It is important to note that if we can achieve partial predictability of SAR models in this situation and build on iterative adjustments of our underlying molecular design hypothesis, we can gradually approximate the underlying function. This process is referred to as 'adaptive design' or 'active learning' (Refs 161,162). The key requirement for active learning is rapid feedback, and for hit and lead discovery, rapid feedback can be achieved by fast synthesize-and-test cycles. be457b7860
tailstock assembly drawing pdf download
Words Of Comfort For Times Of Loss: Help And Hope neueste windos gitar
cara upgrade windows 7 sp1 ke sp2
Das iPad braucht Nachhilfe aus dem Web