Reduce Production Time Research Articles

Involvement of frontline clinicians in healthcare technology development: Lessons learned from a ventilator project

Co-development of healthcare technology with users helps produce user-friendly products, ensuring safe device usage and meeting patients’ needs. For developers considering healthcare innovations, engaging user experience can reduce production time and cost while maximizing device application. The purpose of this paper is to report lessons learned from the development of a 3D printed origami ventilator prototype in response to the rise of ventilator demand due to the Coronavirus disease (COVID-19) pandemic. We conducted focus groups with frontline clinicians working in an Intensive Care Unit of a large urban hospital in Vancouver, British Columbia, Canada. In the interdisciplinary focus groups, we identified challenges, practical tips about product development, the human needs of technology, and cross-discipline peer learning. The focus group discussions provide useful insight into the technology development for complex clinical contexts. Based on our experiences, we articulate five practical tips for co-development of healthcare technology - AGILE: Analyse users’ needs first, Gain insights into complex context, Involve users early and frequently, Lead with a prototype, and Educate and support. Through sharing the tips and lessons learned, we wish to emphasize the necessity of meaningful multi-disciplinary collaboration during healthcare technology development and promote the inclusion of frontline clinicians during these initiatives.

Auxins and Cytokinins elicit a differentiated response in the formation of shoots and roots in Cattleya maxima Lindl and Phalaenopsis amabilis (L) Blume

Orchids have a long period of production and need to be under the right conditions. Therefore, in vitro propagation is an alternative to reduce production time. This study aimed to determine the optimal in vitro propagation conditions in two species of orchids, Cattleya maxima Lindl. and Phalaenopsis amabilis (L.) Blume. For this goal, two experiments were carried out: multiplication and rooting, for both species. In the multiplication experiment, the treatments were differentiated according to the complement of MS (Murashige and Skoog medium), banana flour, kinetin or 6-Benzylaminopurine (BAP). Meanwhile, in the rooting experiment, the complements were banana flour, Indol-3-butiric acid (IBA), Naphthaleneacetic acid (NAA) or 2,4-Dichlorophenoxyacetic acid (2,4-D). According to the results, banana flour (50 g.L-1) increased the plantlet height in C. maxima (multiplication) and P. amabilis (rooting). Kinetin (0.003 g.L-1) and BAP (0.005 g.L-1) increased the number of shoots and leaves in C. maxima, in multiplication experiment. In rooting experiment, NAA (0.003 g.L-1) increased significantly the number of leaves in C. maxima. The root formation was notably stimulated by banana flour in both species. Likewise, 2,4-D (0.003 g.L-1) can cause phytotoxic effects and inhibit root formation. In conclusion, C. maxima was more robust than P. amabilis since in all studied variables the former presented the highest values. Furthermore, C. maxima responds adequately to application of kinetin and BAP to increase the aerial part of the plant, however, they inhibit the root formation. In contrast, kinetin and BAP do not limit root growth in P. amabilis.

The power of genetics: Past and future contribution of balanced genetic selection to sustainable growth and productivity of the Norwegian Atlantic salmon (Salmo salar) industry

In less than 50 years Atlantic salmon farming has become a major industry in Norway, and several attempts at predicting future growth have been made. In all food production, genetics has played an important role in production optimisation and efficiency. The benefits of genetic selection in aquaculture are less documented due to a variable production environment, long production time and challenges such as sea-lice, infectious diseases, and market fluctuations. This study investigates the impact of 50 years of balanced genetic selection on growth and productivity of the Norwegian Atlantic salmon industry. In a common garden experiment eggs from females representing present-day farmed Atlantic salmon (generation 11), were fertilised with cryo-preserved milt from males representing previous generations, and the progeny (half-sib families) were reared under the same commercial farming conditions. At the termination of this experiment, the difference in average body weight between generation 0 and half-sibs from present-day salmon was 1.5 kg. The results from the common garden experiment were used to model the contribution of genetic selection for increased salmon growth rate to the industry from generation 0 (harvested in 1975–1978) to generation 11 (harvested in 2017 – 2019) and to simulate the power of genetics for expected industry growth until 2050 (generation 24) giving four different forecast scenarios. We found that a total Norwegian production of 2.5 to 3.2 million tonnes could be achieved in 2050 solely by improving the inherent growth potential of Atlantic salmon through genetic selection. Further, the production time in seawater is expected to be reduced by 40 to 53% from 15.3 months in 2019 to a production time ranging from 9.2 to 7.2 months in 2050, respectively. Reduced production time in seawater results in reduced time at risk of diseases and other unfortunate events, and in our most progressive scenario, mortality in seawater was expected to be reduced by up to 50%. The contribution to reduced losses and increased resource utilisation, increases the sustainability of the Norwegian aquaculture industry. This study demonstrated that it is possible to significantly increase the Norwegian aquaculture production by genetic improvement, and that contribution from genetic selection for growth rate alone can close 77% of the gap of the forecasted five million tonnes in 2050. These scenarios demonstrate that genetic selection should be included as a central component when predicting future development and growth of aquaculture production, both in Norway and other salmon producing countries.

Discrete-Events Simulation for Teaching Statistics in Industrial Engineering

This paper presents a discrete events simulation tool developed to support undergraduate students in their Statistics and Data Analysis course. Although the use of modern smart technologies in the industry contributes to a profusion of data, very few enterprise datasets are freely available, resulting in a serious lack of open real-world data for research and education. To overcome this difficulty, we designed a tool that simulates scheduling scenarios in a manufacturing environment. The generated data may be used to put statistical concepts and methods into practice to design cost-effective strategies for optimizing key performance indicators, such as reducing production time, improving quality, eliminating wastes, and maximizing profits. Keywords: industrial datasets, teaching statistics, discrete events simulation

Vision sensor based residual vibration suppression strategy of non-deformable object for robot-assisted assembly operation with gripper flexibility

PurposeIndustrial robots are extensively deployed to perform repetitive and simple tasks at high speed to reduce production time and improve productivity. In most cases, a compliant gripper is used for assembly tasks such as peg-in-hole assembly. A compliant mechanism in the gripper introduces flexibility that may cause oscillation in the grasped object. Such a flexible gripper–object system can be considered as an under-actuated object held by the gripper and the oscillations can be attributed to transient disturbance of the robot itself. The commercially available robots do not have a control mechanism to reduce such induced vibration. Thus, this paper aims to propose a contactless vision-based approach for vibration suppression which uses a predictive vibrational amplitude error-based second-stage controller.Design/methodology/approachThe proposed predictive vibrational amplitude error-based second-stage controller is a real-time vibration control strategy that uses predicted error to estimate the second-stage controller output. Based on controller output, input trajectories were estimated for the internal controller of the robot. The control strategy efficiently handles the system delay to execute the control input trajectories when the oscillating object is at an extreme position.FindingsThe present controller works along with the internal controller of the robot without any interruption to suppress the residual vibration of the object. To demonstrate the robustness of the proposed controller, experimental implementation on Asea Brown Boveri make industrial robot (IRB) 1410 robot with a low frame rate camera has been carried out. In this experiment, two objects have been considered that have a low (<2.38 Hz) and high (>2.38 Hz) natural frequency. The proposed controller can suppress 95% of vibration amplitude in less than 3 s and reduce the stability time by 90% for a peg-in-hole assembly task.Originality/valueThe present vibration control strategy uses a camera with a low frame rate (25 fps) and the delays are handled intelligently to favour suppression of high-frequency vibration. The mathematical model and the second-stage controller implemented suppress vibration without modifying the robot dynamical model and the internal controller.

PENINGKATAN PRODUKTIFITAS PADA UNIT PRODUKSI GULA SAGU UP2K SUNGAI TOHOR MELALUI PENERAPAN TEKNIK PENGERINGAN DENGAN CABINATE DRYER SAGO SUGAR

Sungai Tohor Village is located in East Tebing Tinggi District, Meranti Islands Regency, Riau Province which has great potential in the field of processing sago plants. In Sungai Tohor Village, there is a Family Income Improvement Business unit (UP2K) which has produced and marketed processed sago palm products in the form of sago sugar. Sago sugar has more advantages compared to other types of sugar besides its relatively cheap price, this sugar also has a low glycemic level making it suitable for people with diabetes. However, high market demand with low production capacity makes it difficult for sago sugar to meet the target. These problems arise because the production time of sago sugar is quite long around 4-5 days, this is due to the traditional drying of sugar because there is no technology that can be used in drying sago sugar effectively and efficiently. The purpose of this activity is to transfer knowledge and assistance in the process of drying the product. The method used is training and practice by the PKM-PI team at the University of Riau regarding the use of drying technology with the Cabinet Dryer Sago Sugar (CADESAR) drying device. Drying technology using CADESAR can improve the quantity and quality of sago sugar products. This technology can reduce production time by using a pressurized hot steam drying system, as one of the drying methods in the modern sugar industry, so that it can increase the amount of production and be more hygienic. The results of the training show that UP2K members can understand and practice knowledge of drying technology well. It is hoped that this will increase the productivity of their sago sugar business.

Microstructure Evolution in Inconel 718 Produced by Powder Bed Fusion Additive Manufacturing

Inconel 718 is a precipitation strengthened, nickel-based super alloy of interest for the Additive Manufacturing (AM) of low volume, complex parts to reduce production time and cost compared to conventional subtractive processes. The AM process involves repeated rapid melting, solidification and reheating, which exposes the material to non-equilibrium conditions that affect elemental segregation and the subsequent formation of solidification phases, either beneficial or detrimental. These variations are difficult to characterize due to the small length scale within the micron sized melt pool. To understand how the non-equilibrium conditions affect the initial solidification phases and their critical temperatures, a multi-length scale, multi modal approach has been taken to evaluate various methods for identifying the initial phases formed in the as-built Inconel 718 produced by laser-powder bed fusion (L-PBF) additive manufacturing (AM). Using a range of characterization tools from the bulk differential thermal analysis (DTA) and x-ray diffraction (XRD) to spatially resolved images using a variety of electron microscopy tools, a better understanding is obtained of how these minor phases can be properly identified regarding the amount and size, morphology and distribution. Using the most promising characterization techniques for investigation of the as-built specimens, those techniques were used to evaluate the specimens after various heat treatments. During the sequence of heat treatments, the initial as-built dendritic structures recrystallized into well-defined grains whose size was dependent on the temperature. Although the resulting strength was similar in all heat treated specimens, the elongation increased as the grain size was refined due to differences in the precipitated phase distribution and morphology.

ОЩАДЛИВЕ ВИРОБНИЦТВО ЯК ІНСТРУМЕНТ ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ ПРОЕКТНОГО МЕНЕДЖМЕНТУ

This article presents the concept of lean production and its practical use in project management. This concept is a combination of principles of building relationships between employees, which creates effective relationships in companies. The efficiency of implementing the concept of lean production is reflected in the reduction of production time or optimization of service delivery, increase productivity, reduce downtime in the enterprise, and reduce inventory. This concept's impact is enhanced by the use of methods that are designed to overcome crises and adverse situations in enterprises. It can minimize the company's costs and create additional value for consumers. The ever-increasing competition requires business leaders to find and apply methods that can increase competitiveness in the long run. Proper implementation of the concept of lean production is a source of competitive advantage. One of the most effective ways to improve the production process's management is to use a combination of several concepts that have the same features or complementary components. The possible solution of dealing with project complexity is the usage of lean production and agile combination. Using a lean production system is not an easy task. The main source of increasing the project's efficiency is the assessment of economic efficiency and the selection of those projects that provide a maximum economic effect. Successful implementation of such projects can create a basis for further improvement of the company. Combining the elements of both Lean and Agile methodologies brings the best practices into the projects managing. Muda is a key term, which is used in the concept of lean production. This term means means all unreasonable costs that do not create added value for the product or service. Because of this, the main idea of this concept arose, which emphasizes the need to produce only the amount of products that consumers need and to abandon mass production. It should be noted that the use of the concept of lean production does not increase the company's cash turnover, because the main purpose of this approach is to reduce costs and increase the efficiency of equity management.

Desalination Plant Performance Prediction Model Using Grey Wolf Optimizer Based ANN Approach

The present era of advances in desalination plants revolves around the involvement of artificial intelligence techniques in ameliorating their modeling and operational performance. Among the two objectives, an accurate modeling of the plant&#x2019;s behavior may certainly help the design engineers to operate the plant in more stable and controlled operating conditions so as to achieve higher plant efficiency. Furthermore, this helps eliminate the risk to the operator&#x2019;s life and reduces production time, energy, and money. From the literature, it is observed that Artificial Neural Network (ANN) has been the most extensively used approach for modeling and simulation of the desalination plant. However, ANN has the concept of biases and weights updation for better prediction and accuracy of the predicted model, but the conventional methods do not yield desirable results. So, the updation of biases and weights using optimization algorithms is preferred in the literature. Therefore, this paper presents the Grey Wolf Optimizer based ANN (GWO-ANN) approach for desirable prediction and accuracy of models. Further, six models (GWO-ANN Model-1 to Model-6) are proposed to more accurately predict the Reverse Osmosis (RO) desalination plant&#x2019;s performance. For this investigation, we have considered four experimental inputs (feed water salt concentration, condenser inlet temperatures, evaporator inlet temperatures, and feed flow rate) and one output (permeate flux). The simulation results predict output performance in quite proximity to the experimental datasets. The simulated hybrid GWO-ANN models (best of best results of GWO-ANN Model-2: <inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} \,\,=98.9$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.007) show superior results than the reported results from the existing Response Surface Methodology (RSM) (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} \,\,=98.5$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.100) and ANN models (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =98.8$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.060) and other PSO-ANN Model (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} \,\,=96.3$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.025) and GA-ANN model (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =98.7$ </tex-math></inline-formula>&#x0025;, Error &#x003D; 0.008). This research exemplifies the involvement of superior nature-inspired intelligent techniques in this modern era to further enhance the savings in production time, energy, and investment of desalination plants.

Broad Learning System Based on Binary Grey Wolf Optimization for Surface Roughness Prediction in Slot Milling

Surface roughness is considered to be one of the most significant indicators for evaluating machining quality. Manufacturing industries generally aim to achieve high product quality while considering reducing production costs and time. Therefore, an accurate and reliable surface roughness prediction model plays a key role in the production process. Recently, a broad learning system (BLS) has attracted great attention due to its outstanding performance. In order to improve the prediction accuracy of surface roughness and reduce the influence of uncertain factors, sensors are necessarily added to monitor the product processing. Consequently, the surface roughness data obtained in the actual machining process tends to have a small sample size and a high feature dimension. In addition, due to the structural characteristics of the BLS, its hidden layer neuron nodes often carry some irrelevant or redundant features during the training process, which affects the prediction performance of the model. To overcome these problems, this article proposes a novel BLS architecture for surface roughness prediction, in which binary grey wolf optimization (BGWO) is used for the selection of neurons in the hidden layer. To verify the effectiveness of the proposed method, a slot milling surface roughness experiment was performed and compared with other methods for different evaluation indicators. The results show that the proposed method provides an effective solution for surface roughness prediction.

Advances in 3D printing of food and nutritional products.

The main advantage of both 3D printing (3DP) and 3D food printing (3DFP) over other technologies is the enormous capacity of both techniques for customization. Its use makes it possible to obtain products without planning and implementing a complex and costly manufacturing process. This makes 3DFP a technology of choice for the preparation of food products that meet specific needs, such as controlled nutritional or rheological properties. However, further technological developments are still needed before 3DFP can be considered fully useful for innovative and demanding applications. If both preparation and post-processing of materials based on 3D printing are optimized, aiming to reduce production time and/or complication for non-expert users, this would open a whole new range of possibilities. It is in this sense that the development of advanced 3DFP systems becomes a must. This chapter reviews current advances in extrusion-based 3D food printing systems, with in situ gelation and mixing as key aspects to better exploit the potential of 3DFP. On one hand, 3DFP systems based on in situ gelation (G3DFP) provide greater control over the final properties of the printed products, as the selection of adequate printing parameters gives the possibility of influencing the gelation process. On the other hand, mixing is indispensable for true 3DFP automation, so that the formulations do not have to be prepared by the user. Different innovative 3DFP systems based on gelling and/or mixing are presented in this chapter. Finally, the status and future of extrusion-based 3DFP, and its application in the production of customized foods for specific needs, are also overviewed.

Improving the Selected Stages of Integrated-Optic Chip Structure Formation and Its Interfacing with Optical Fibers

The paper considers experimental technological regimes at different stages of integrated optical chips creation for modern applications: optoelectronics, fiber-optic sensors, microfluidics. The considered experimental regimes demonstrate not only qualitative, but also quantitative efficiency: at the stage of the ridge waveguides formation, there is a possibility of achieving high etching rates of lithium niobate - up to 950 nanometers per minute in fluorine-containing plasma with an increased consumption of plasma gases without forced thermostating of the sample. Using scanning electron microscope, structural changes in the surface have been demonstrated: clustering of surface etching foci, indicating the uniformity of etching at the macroscale, and changes in the thickness and structure of the near-surface defect layer, indicating the possibility of its complete removal by the plasma-chemical method. The method can be applied both for the manufacturing of ridge waveguides and optical elements based on them, and for microfluidics elements. Then, the efficiency of the waveguides and optical fibers interfacing method has been confirmed with frequency domain reflectometry. The algorithmic details of the interfacing by the modified Newton's method are also described. The modified Newton’s method allowed to simplify the alignment process by performing coarse alignment based on reflections from the far end face of the waveguide, speed up the alignment process at fine mode, and fully automate the alignment of the waveguide and optical fiber at any initial displacements. All the proposed technological process modernization leads to the improvement of technological characteristics and the individual stages production time reduction.

A PROPELLER BLADE MANUFACTURING BY HYBRID ADDITIVE MANUFACTURING SYSTEM

Hybrid Additive Manufacturing (Hybrid-AM) describes multi-operational or multi-functional additive manufacturing systems. In industry, the increasing tendency in applications of Hybrid-AM brings up the challenge of improving novel methods for the manufacturing of new or hybrid parts. Hybrid AM can produce fully functional assemblies without any assembly operation. In this study, the hybrid additive manufacturing system means that an object is to be designed partly made from prefabricated or off-the-shelf parts and added by the WAAM process. For this purpose, a prototype Hybrid-AM system using the pulsed TIG-Wire-Arc technique was designed and constructed. The constructed SMD system has three drivers on the x, y, and z-axes and an additional rotary driver (fourth axis). Using the Hybrid-AM machine the wire form material can be deposited on an existing primitive profile i.e., a rod, pipe, a profile, or any 3D surfaces with reducing production time. In this way, spiral-shaped features or twisted blade shapes can be added to cylindrical parts. In this study, a stainless- steel propeller blade was deposited on a pipe by using the developed prototype Hybrid AM machine. A non-planar toolpath was used to deposition the subsequent layers and the surface of the propeller blade was finished using 4-axis CNC machining.

A convolution-based approach to cold spray additive manufacturing

Cold Spray Additive Manufacturing (CSAM) is a well-established technology that has recently attracted interest for forming 3D shapes in a fast and scalable fashion. Nonetheless, the resulting surface of cold sprayed parts normally requires post-deposition machining to achieve the desired surface finish. In this work, a convolution-based digital framework for CSAM yield and surface finish prediction able to calculate the optimal interline distance to reduce surface waviness was developed. The aim is to minimise post-deposition treatments, thereby reducing production time, material waste and costs. This method is applicable beyond CSAM and can be of interest for other additive manufacturing techniques.

Development of Kilogram-Scale Convergent Liquid-Phase Synthesis of Oligonucleotides.

Oligonucleotide drugs show promise to treat diseases afflicting millions of people. To address the need to manufacture large quantities of oligonucleotide therapeutics, the novel convergent liquid-phase synthesis has been developed for an 18-mer oligonucleotide drug candidate. Fragments containing tetra- and pentamers were synthesized and assembled into the 18-mer without column chromatography, which had a similar impurity profile to material made by standard solid-phase oligonucleotide synthesis. Two of the fragments have been synthesized at ∼3 kg/batch sizes and four additional tetra- and pentamer fragments were synthesized at >300-g scale, and a 34-mer was assembled from the fragments. Critical impurities are controlled in the fragment syntheses to provide oligonucleotides of purities suitable for clinical use after applying standard full-length product purification process. Impurity control in the assembly steps demonstrated the potential to eliminate chromatography of full-length oligonucleotides, which should enhance scalability and reduce the environmental impact of the process. The convergent assembly and telescoping of reactions made the long synthesis (>60 reactions) practical by reducing production time, material loss, and chances for impurity generation.

A machine learning-based framework for automatic identification of process and product fingerprints for smart manufacturing systems

Process and product fingerprints are unique process and product characterisation parameters, respectively. They need to be controlled to ensure that manufactured parts are within their specifications to achieve the required functions. The process and product fingerprints are developed to reduce production time and efforts for metrology and process optimisation. The existing methods to identify process and product fingerprints are usually developed for particular machining processes. They are often performed manually which make them time-consuming. In addition, the existing methods are strongly influenced by human decisions. As a result, the fingerprints identified by these methods may have poor correlations with product functionality or the surface quality/dimension characteristics. For the first time, a unique machine learning-based framework is presented in this paper to automatically identify process and product fingerprints. The new framework can shorten fingerprint development time and reduce complexity of process control and product characterisation. A CTER index which is the ratio of correlation and testing error ratio has been proposed to determine the best fingerprint in the framework. The performance of the proposed framework has been validated using data obtained from manufacturing superhydrophobic structures on stainless steel using a nanosecond laser. The correlation of product functional characteristics with fingerprints identified using the proposed framework is observed better than existing fingerprints developed purely based on the physics of the machining process. In addition, the proposed framework can also be used for process optimisation and prediction of functional characteristics/measurement tolerances, which has also been presented in this paper.

Mechanical properties comparison of PLA, tough PLA and PC 3D printed materials with infill structure – Influence of infill pattern on tensile mechanical properties

One of the advantages provided by fused deposition modelling (FDM) 3D printing technology is the manufacturing of product materials with infill structure, which provides advantages such as reduced production time, product weight and even the final price. In this paper, the tensile mechanical properties, tensile strength and elastic modulus, of PLA, Tough PLA and PC FDM 3D printed materials with the infill structure were analysed and compared. Also, the influence of infill pattern on tensile properties was analysed. Material testing were performed according to ISO 527-2 standard. All results are statistically analysed and results showed that infill pattern have influence on tensile mechanical properties for all three materials.

Effect of ultrasound‐assisted hydrodistillation on extraction kinetics, chemical composition, and antimicrobial activity of Citrus jambhiri peel essential oil

AbstractCitrus jambhiri processing generates 40% peel waste with considerable waste management constraints. Interestingly, these peels are a rich source of essential oil. Hence, in this study, the essential oil was extracted from fresh peels and peel powder (PP) of C. jambhiri using hydrodistillation (HD) and ultrasound‐assisted hydrodistillation (USHD). Kinetic study, physicochemical, antimicrobial, and chemical composition of the essential oil obtained from PP and fresh peel with albedo (FPA) and without albedo (FPWA) using HD and USHD was studied. The kinetic study was further experimentally validated using the first and second‐order kinetic model. The integrated equations were used to test the fitting of the model with obtained experimental data. Kinetics of oil extraction from PP using USHD showed that extraction process best fitted in second‐order kinetic model with a high coefficient of determination (R2 = .999), yield (3.63%) and extraction rate constant (K2 = 0.0306), showing that it validated the process thoroughly. The physicochemical properties of essential oils obtained from both extraction methods fall within an acceptable range. Specific gravity, acid value, and total phenolic content showed no significant difference between both extraction methods while others differed significantly. The antioxidant and antimicrobial activity increased with ultrasound treatment. GC–MS analysis of different oil samples revealed that 10 compounds extracted from each extraction with the highest amount of d‐limonene (61.29%) were obtained from PP using USHD. USHD stands as a fast, efficient, and economical method for extracting essential oil and can be a potential source for nutraceutical and pharmaceutical industries.Practical ApplicationCitrus jambhiri peels are discarded as waste material. Its comprehensive utilization is urgently needed. Essential oil from citrus peels is an important biologically active substance containing tens to hundreds of bioactive compounds with health‐promoting characteristics. Ultrasound‐assisted hydrodistillation is a promising extraction technique to enhance product quality and reduce production time and consumed energy. Application of the novel proposed methods combined with conventional or alternative to increase the extraction rate on an industrial scale could be recommended.

A Method to Optimise Semi-Trailers with the Use of FEM Analysis and Durability Tests with a Road Simulator

This paper presents a method developed by Wielton company – a manufacturer of trailers and semi-trailers – for the optimization of semi-trailer’s construction, supported by FEM analysis and the modern road simulator – a test stand included as part of the method for optimising semi-trailers. The aim of the research was to optimize the process of developing a technical measure in terms of shortening the time of implementing new products while guaranteeing high durability and functionality of the products. As a result of the optimization of the construction with the use of FEM analysis and the road simulator, the semi-trailer as a new product for sale is delivered quickly and possible defects of construction are eliminated on early stage of new product development. The method assumes the following stages of the development process: determination of input data to the process, development of a 3D model, FEM numerical analysis (static and dynamic), development of an optimized 3D model based on numerical analyses, construction of the first prototype, prototype validation tests using the road simulator and other test stands. Important feature of the developed method to optimise construction is connected with specific data achieved from FEM analysis and used in tests with the road simulator. The effect of using presented method is the elimination of 98% errors in construction and technology. These errors concern not only the key parts of the semi-trailer, i.e. the body and the frame, but also less important components, i.e. fixing of tanks and boxes, ladder, spare wheel basket, extinguisher mounting. The method to optimise semi-trailers developed in Wielton have many advantages, such as: limiting the risk of launching breakthrough solutions on the market, faster increase in vehicle competitiveness, optimizing vehicle construction, reduction of production time and costs, and as a consequence - faster implementation of new products, reducing the number of complaints and service costs, increasing the value and prestige.

A scale-up strategy for the synthesis of chitosan derivatives used in micellar nanomedicines

Nanomedicines have been increasingly investigated and used by pharmaceutical industry due to their potential in solving various public health problems. However, standardizing and approving nanomedicines remains a significant challenge, as the translation from the laboratory to the market is still limited. These constraints are due to a lack of reproducibility and standardization of procedures, small batch sizes due to inability to scale-up, or the associated production costs as a result of the production methods chosen. In this work, two chitosan derivatives, methoxypolyethylene glycol-chitosan (mPEG-CS) and methoxypolyethylene glycol-chitosan-oleic acid (mPEG-CS-OA), produced at the lab scale were implemented in a pharmaceutical industry to achieve the scale-up production using cross flow filtration (CFF). The two copolymers were shown to be capable of retaining their physicochemical properties when produced in larger batch sizes, with reduced production time and increased yield. Also, both chitosan derivatives presented no in vitro cytotoxicity independent of the method of production. Furthermore, after scale-up, polymeric micelles produced from mPEG-CS-OA were tested for storage stability, demonstrating that micelles remained stable at – 20 °C for at least 6 months. This study demonstrated the feasibility of producing polymers and polymeric micelles closer to the bedside due to their suitability for GMP production.