Detailed Process Information Research Articles

Interactive Health Technology Tool for Kidney Living Donor Assessment to Standardize the Informed Consent Process: Usability and Qualitative Content Analysis.

Kidney living donation carries risks, yet standardized information provision regarding nephrectomy risks and psychological impacts for candidates remains lacking. This study assesses the benefit of interactive health technology in improving the informed consent process for kidney living donation. The Kidney Hub institutional open portal offers comprehensive information on kidney disease and donation. Individuals willing to start the kidney living donation process at Helsinki University Hospital (January 2019-January 2022) were invited to use the patient-tailored digital care path (Living Donor Digital Care Path) included in the Kidney Hub. This platform provides detailed donation process information and facilitates communication between health care professionals and patients. eHealth literacy was evaluated via the eHealth Literacy Scale (eHEALS), usability with the System Usability Scale (SUS), and system utility through Likert-scale surveys with scores of 1-5. Qualitative content analysis addressed an open-ended question. The Kidney Hub portal received over 8000 monthly visits, including to its sections on donation benefits (n=1629 views) and impact on donors' lives (n=4850 views). Of 127 living kidney donation candidates, 7 did not use Living Donor Digital Care Path. Users' ages ranged from 20 to 79 years, and they exchanged over 3500 messages. A total of 74 living donor candidates participated in the survey. Female candidates more commonly searched the internet about kidney donation (n=79 female candidates vs n=48 male candidates; P=.04). The mean eHEALS score correlated with internet use for health decisions (r=0.45; P<.001) and its importance (r=0.40; P=.01). Participants found that the Living Donor Digital Care Path was technically satisfactory (mean SUS score 4.4, SD 0.54) and useful but not pivotal in donation decision-making. Concerns focused on postsurgery coping for donors and recipients. Telemedicine effectively educates living kidney donor candidates on the donation process. The Living Donor Digital Care Path serves as a valuable eHealth tool, aiding clinicians in standardizing steps toward informed consent. ClinicalTrials.gov NCT04791670; https://clinicaltrials.gov/study/NCT04791670. RR2-10.1136/bmjopen-2021-051166.

Bio-decarbonization by microalgae: a comprehensive analysis of CO2 transport in photo-bioreactor

Microalgae biofilm is a typical porous structure where CO2 is converted by microalgae into organic matter. Therefore, CO2 transport and its distribution in porous biofilm are crucial for microalgae decarbonization and its energy utilization. In order to get detailed process information of CO2 transport and its bioconversion, a mathematical model considering the microalgae growth and its material consumptions was established for substances' flow and transport processes in an immersed microalgae biofilm reactor. Modeling results showed that CO2 concentration on the surface of biofilm reduced about 26.57% along the flow direction. Increased inlet CO2 concentration (0.2–5.2 ​mM) significantly promoted the average specific growth rate of biofilm, which was more dramatical at a low flow rate with an enhancement about 7.38 times. Essential reason for it is a synchronous increase on total transfer flux of CO2 (8.25 times by ϕ_CO2) and average CO2 consumption rate (7.48 times by ACR_CO2) in biofilm. However, such promotion gradually waned with a growing initial carbon supply concentration. Enhanced CO2 transport in biofilm caused by increasing culture medium's flow rate (1–6 ​mL ​min−1) doesn't always result in synchronous improvements on biofilm growth. At sufficient carbon supply, increased flow rate doesn't further effectively improve biofilm growth but greatly reduced CO2 removal. Whether increasing carbon supply concentration or flow rate, biofilm growth can't be significantly promoted unless the ϕ_CO2 and ACR_CO2 in biofilm were increased by almost the same level. This work provides a new and deeper mechanistic insight into macroscopic growth characteristics of biofilms from the perspective of CO2 transport in it, as well as providing some theoretical guidance towards the cultivation of immersed microalgae biofilm for bio-decarbonization.

Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development

AbstractHydrological process knowledge has advanced significantly during the past six decades. During the same period catchment models have undergone major developments including simple black box models, lumped conceptual models, hydrological response unit models, spatially distributed process‐based models and, recently, the emergence of machine learning hybrid models. This development has been enabled by improved understanding of hydrological processes together with ever increasing computer power and improved availability and accessibility of data. During the first couple of decades, a key assumption motivating the development towards increasing complexity of model codes was that more detailed process description would lead to more accurate model simulations and enable prediction of impacts from human activities that previous models were not able to provide. Subsequently, scientific tests showed that this is very often not the case, leading towards a recognition of the importance of careful model evaluation accounting for key uncertainties in data, model parameters and model conceptual understanding. We have reviewed 54 model studies from the past 60 years and characterized them with respect to model type, spatial discretization and model evaluation techniques. This showed clear development trends and different strategies for enhancing hydrological process knowledge in models. In addition, we present a case study, where we use two models for the same catchment. The models are identical except for the spatial discretization of 100 m and 500 m, respectively. The two models have an apparent equal performance measured against standard calibration metrics, but nevertheless show large differences when considering detailed process information such as partitioning of streamflow components and water table depth patterns, that was not considered during the model calibration process. The paper discusses perspectives for enhancing hydrological process knowledge in future catchment modelling concluding that the emergence of big data is likely to become a major game changer.

An Empirical Benchmark for Resource Use in Fused Deposition Modelling 3D Printing of Isovolumetric Mechanical Components

The lack of detailed process information such as part shape, process setups, and process efficiencies limit the accuracy of life cycle inventory models that are used for estimating environmental impacts of manufacturing processes. Such limitations are particularly true for processes such as additive manufacturing, where part shape significantly influences the resulting process characteristics. To address the above knowledge gap, this paper investigates the influence of part shape on process inventory of fused deposition modeling 3D printing. To this end, we experimentally measured the resource consumption for printing 68 different isovolumetric mechanical components with identical print settings. These results are presented in a structured form, titled “Resource Use for 3D Printing Isovolumetric Mechanical Components (r3DiM) Benchmark”, that includes our experimental data, 3D model files, NC code used for printing the models, and layer-wise energy consumption of the heaters of the bed & extruder, material use, and other printing peripherals of each object. It is our hope that the r3DiM benchmark will serve as a reference of future researchers interested in creating shape-aware, predictive life cycle inventory models for fused deposition modeling 3D printing processes. The r3DiM benchmark can be accessed from the following url: https://tinyurl.com/r3DiMBenchmark.

Systematic Approach for Synthesizing Carbon–Hydrogen–Oxygen Networks Involving Detailed Process Simulations

Carbon–hydrogen–oxygen symbiotic networks (CHOSYNs) provide a systematic multiscale framework for integrating multiple hydrocarbon processing plants. Earlier approaches have focused on high-level performance benchmarking using atomic and stoichiometric targeting techniques, which are subsequently detailed to identify the implementation strategies for allocation and processing. These sequential approaches are very powerful for the cases involving benchmarks that are independent of the system details (e.g., minimum usage of raw materials, minimum discharge of wastes, maximum revenue). For objectives involving detailed process information (e.g., capital cost minimization), there is a need to include the system details early enough in optimization. This paper proposes a systematic approach for incorporating detailed process simulations throughout the various stages of synthesizing a CHOSYN. The proposed approach involves the use of computer-aided process simulators and simultaneous optimization for synthesizing a CHOSYN. Critical data are generated and used at each design stage. For existing facilities, targeting uses the simulated data on available resources and sinks (e.g., flow rate, composition, pressure, and temperature). Process synthesis is used to generate a set of candidate new units and plants to be added, while detailed simulation is used to size these plants and identify the specific needs for flows and compositions. The proposed approach is implemented for a case study that involves five existing plants and a set of seven candidate plants that can induce symbiosis. The results show final configurations with the selection of new plants, the reduction of raw material, the allocation of internal and external sources, and the details of process information, capital and operating costs, and flow rate, pressure, and temperature of the exchangeable streams across the network.

On the role of complexity in machining time estimation

Early cost estimation of machined parts is difficult as it requires detailed process information that is not usually available during product design. Parametric methods address this issue by estimating machining time from predictors related to design choices. One of them is complexity, defined as a function of dimensions and tolerances from an analogy with information theory. However, complexity has only a limited correlation with machining time unless restrictive assumptions are made on part types and machining processes. The objective of the paper is to improve the estimation of machining time by combining complexity with additional parameters. For this purpose, it is first shown that three factors that influence machining time (part size, area of machined features, work material) are not fully captured by complexity alone. Then an optimal set of predictors is selected by regression analysis of time estimates made on sample parts using an existing feature-based method. The proposed parametric model is shown to predict machining time with an average percentage error of 25% compared to the baseline method, over a wide range of part geometries and machining processes. Therefore, the model is accurate enough to support comparison of design alternatives as well as bidding and make-or-buy decisions.

An eco-design for additive manufacturing framework based on energy performance assessment

Additive manufacturing (AM) has been considered as a promising technology with higher resource efficiency and better ecological benefits in production systems. However, recent works have shown that the ecological benefits of AM are sensitive to the parameters in operation. If the parameters are not designed appropriately, the ecological performance of AM can be worse than conventional manufacturing processes. To ensure the ecological benefits of AM, eco-design based on Life Cycle Assessment (LCA) is a promising approach to analyze and minimize the environmental impacts of AM. However, LCA can only be carried out at the later stage of the design process after most design and decision operations are already made because the implementation of LCA requires detailed process and inventory information of the entire life cycle. If users attempt to optimize the ecological performance of their design solutions, they need to repeat almost the entire design process. To solve this problem, this work suggests using energy performance assessment to replace LCA in eco-design for AM. The proposed approach uses a holistic framework consisting of three parts: a simulation tool for energy consumption prediction of AM, an assessment model for energy performance of AM, and general workflows of eco-design for AM. Since the energy performance quantification and assessment of AM require less process information, it can be integrated earlier and easier into the eco-design for AM. Additionally, an example of use case is provided that confirms the feasibility of this framework.

Explaining the Process of Determining Futility Increases Lay Public Acceptance.

Rationale: National guidelines have laid out a process to conflict resolution in cases of potentially inappropriate medical interventions. Objectives: To determine the association between information about a process-based approach and lay public perceptions of the appropriateness of withholding medically inappropriate interventions. Methods: Respondents from a nationwide sample completed a survey with two adult intensive care unit-based vignettes: one about advanced cancer where doctors told the family that additional chemotherapy would not be offered, and a second case of multiorgan failure after brain hemorrhage where dialysis would not be offered. Participants were randomly assigned to see or not see information about a detailed process for the determination to withhold (second opinion, ethics consultation, exploring transfer to another institution). The primary outcome was the perceived appropriateness of not providing the treatment (four-point scale, dichotomized for analysis, modified Poisson regression), and the secondary outcome was the negative emotional reaction to the case (positive and negative affect schedule, range 1-5, higher is greater negative emotional response, linear regression). Results: A total of 1,191 respondents were included. Providing detailed process information increased the perceived appropriateness of withholding treatment by approximately 10 percentage points in each vignette: (chemotherapy, 75.7-85.4%; dialysis, 68.0-79.3%). Process information remained associated with perceived appropriateness of withholding treatment after adjustment for order effects and prespecified respondent characteristics (chemotherapy: prevalence ratio, 1.13; 95% confidence interval [CI], 1.07-1.19) (dialysis: prevalence ratio, 1.17; 95% CI, 1.10-1.25). Process information was not associated with emotional response to the cases (chemotherapy: β = -0.04; 95% CI, -0.16 to 0.09) (dialysis: β = -0.02; 95% CI, -0.14 to 0.10; both adjusted for order effects). Conclusions: Providing process-based conflict resolution information increased public acceptance of determinations of medical futility, supporting the practice outlined in national consensus statements.

A Predictive Environmental Assessment Method for Construction Operations: Application to a Northeast China Case Study

Construction accounts for a considerable number of environmental impacts, especially in countries with rapid urbanization. A predictive environmental assessment method enables a comparison of alternatives in construction operations to mitigate these environmental impacts. Process-based life cycle assessment (pLCA), which is the most widely applied environmental assessment method, requires lots of detailed process information to evaluate. However, a construction project usually operates in uncertain and dynamic project environments, and capturing such process information represents a critical challenge for pLCA. Discrete event simulation (DES) provides an opportunity to include uncertainty and capture the dynamic environments of construction operations. This study proposes a predictive assessment method that integrates DES and pLCA (DES-pLCA) to evaluate the environmental impact of on-site construction operations and supply chains. The DES feeds pLCA with process information that considers the uncertain and dynamic environments of construction, while pLCA guides the comprehensive procedure of environmental assessment. A DES-pLCA prototype was developed and implemented in a case study of an 18-storey building in Northeast China. The results showed that the biggest impact variations on the global warming potential (GWP), acidification potential (AP), eutrophication (EP), photochemical ozone creation potential (POCP), abiotic depletion potential (ADP), and human toxicity potential (HTP) were 5.1%, 4.1%, 4.1%, 4.7%, 0.3%, and 5.9%, respectively, due to uncertain and dynamic factors. Based on the proposed method, an average impact reduction can be achieved for these six indictors of 2.5%, 21.7%, 8.2%, 4.8%, 32.5%, and 0.9%, respectively. The method also revealed that the material wastage rate of formwork installation was the most crucial managing factor that influences global warming performance. The method can support contractors in the development and management of environmentally friendly construction operations that consider the effects of uncertainty and dynamics.

Process analysis and economic evaluation of mixed aqueous ionic liquid and monoethanolamine (MEA) solvent for CO2 capture from a coke oven plant

AbstractThis study investigates the process and economic impacts of using an aqueous mixture of 1‐butylpyridinium tetrafluoroborate ([Bpy][BF4]) ionic liquid (IL) and monoethanolamine (MEA) as the solvent for CO2 capture from a coke‐oven plant. The gaps highlighted in the literature on the study of an aqueous mixture of IL and MEA for CO2 capture include lack of detailed process models or information on the impacts of varying the IL concentration on different process conditions and economics. This study addressed these needs by developing a rate‐based, solvent‐based CO2 capture process model with a mixed IL and MEA solvent and using the model to perform process and economic evaluations. The model was developed with Aspen Plus® and was used to investigate seven different aqueous mixtures of IL and MEA. The MEA concentration was 30 wt% for all the seven aqueous solvent mixtures, and the corresponding IL concentration was 0, 5, 10, 15, 20, 25 and 30 wt% for each combination. The hybrid IL solvent mixtures (i.e. 5–30 wt% IL) have 7–9% and 12–27% less regeneration energy and solvent circulation rate respectively compared to the base case (i.e. 30 wt% MEA). Based on a commercial‐scale cost benchmark for the IL, the initial solvent cost for the mixed solution is predictably higher. However, the solvent makeup cost is less for the mixed solvent. © 2018 Society of Chemical Industry and John Wiley &amp; Sons, Ltd.

Assessing the effectiveness of five process elicitation methods: A case study of chemotherapy treatment plan review

To reduce the probability of failures and to improve outcomes of safety-critical human-intensive processes, such as health care processes, it is important to be able to rigorously analyze such processes. The quality of that analysis often depends on having an accurate, detailed, and sufficiently complete understanding of the process being analyzed, where this understanding is typically represented as a formal process model that could then drive various rigorous analysis approaches. Developing this understanding and the corresponding formal process model may be difficult and, thus, a variety of process elicitation methods are often used. The work presented in this paper evaluates the effectiveness of five common elicitation methods in terms of their ability to elicit detailed process information necessary to support rigorous process analysis. These methods are employed to elicit typical steps and steps for responding to exceptional situations in a safety-critical health care process, the chemotherapy treatment plan review process. The results indicate strengths and weaknesses of each of the elicitation methods and suggest that it is preferable to apply multiple elicitation methods.

Miniaturized Reaction Chamber for Optimized Laser-Assisted Carbon Nanotube Growth

The localized growth of carbon nanotube structures has potential in many applications such as interconnects, field emitters and sensors. Using a laser to locally heat the substrate offers a highly versatile process compatible with a broad range of substrates and devices. However, for laser-assisted CNT growth, detailed process information, such as temperature evolution and process monitoring over time are often unavailable. Here, we report on a miniaturized laser-CVD reactor, where a precise control of the gas flow and composition on the laser-growth site is provided. The particular design of this miniaturized reactor results in a high reproducibility as well as faster growth time. A multi-parameter finite element method (FEM) model is implemented to link substrate temperature at the laser spot with emitted radiation, taking into account the gas flow, the process time and other temperature-dependent physical parameters such as forced convection, thermal- conductivity and heat capacity. The resulting growth is assessed using Scanning Electron Microscopy. Combined with results from the thermal model, process information is used to calculate activation energy for the nanotube nucleation. Finally, several demonstrations of possible applications are given, exploiting the potentials of the miniaturized reaction chamber.

Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks

Assembly time estimation is traditionally a time-intensive manual process that requires detailed geometric and process information, which is often subjective and qualitative in nature. As a result, assembly time estimation is rarely applied during early design iterations. In this paper, the authors explore the possibility of automating the assembly time estimation process while reducing the level of design detail required. In this approach, they train artificial neural networks (ANNs) to estimate the assembly times of vehicle subassemblies using either assembly connectivity or liaison graph properties, respectively, as input data. The effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results indicate that this method can provide time estimates of an assembly process with ±15% error while relying exclusively on the geometric part information rather than process instructions.

Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble

Pattern recognition is an important issue in statistical process control because unnatural control chart patterns (CCPs) exhibited on control charts can be associated with specific causes that adversely affect the manufacturing process. In recent years, many machine learning techniques [e.g., artificial neural networks (ANNs) and support vector machine (SVM)] have been successfully used to the CCP recognition (CCPR) in autocorrelated manufacturing processes. However, these existing researches can only detect and classify unnatural CCPs but do not provide more detailed process information, for example the autocorrelation level, which would be very useful for quality practitioners to search the assignable causes that give rise to the out-of-control situation. This study proposes a neural network ensemble-enabled autoregressive coefficient-invariant CCPR model for on-line recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time with unknown constant autoregressive coefficient. In this model, each individual back propagation network (BPN) is trained to recognize only CCPs with the specific autoregressive coefficient of the underlying process, while the outputs of all these individual BPNs are combined via a learning vector quantization network (LVQN). The experimental results indicate that the proposed CCPR model can not only accurately recognize the specific CCP types but also efficiently identify the autoregressive coefficient of the underlying autocorrelated manufacturing process. Empirical comparisons also show that the proposed CCPR model outperform other existing CCPR approaches in literature. In addition, a demonstrative application is provided to illustrate the utilization of the proposed CCPR model to the CCPR in autocorrelated manufacturing processes.

Microbial control of bromocarbon concentrations in coastal waters of the western Antarctic Peninsula

The marine environment is known to be a source of CHBr3 and CH2Br2 and hence ozone-depleting inorganic bromine to the troposphere but, to date, the dominant processes controlling their concentrations in seawater remain poorly understood. Here results are reported from a series of laboratory experiments designed to investigate bromocarbon dynamics in cultures of marine diatoms and bacteria isolated recently from the Rothera Time-Series (RaTS) site located in coastal waters of the western Antarctic Peninsula. The main focus of this work was an isolate of the centric diatom Thalassiosira sp. Different processes were found to control the concentrations of CHBr3 and CH2Br2 in this culture. The production of CHBr3 was restricted to the exponential phase of growth suggesting a link with a primary metabolic process and was a factor of 5–6 higher in cultures treated with antibiotics to reduce bacterial activity. 13CHBr3 additions confirmed that CHBr3 was not subject to significant bacterial breakdown and hence bacteria are likely to be inhibiting the production of this compound. The rate of 13CH2Br2 appearance in the cultures observed following 13CHBr3 addition suggests that the major source of CH2Br2 in the diatom culture was transformation from CHBr3. CD2Br2 additions revealed that CH2Br2 was subject to significant breakdown in cultures of both Thalassiosira sp. and a bacterial isolate with apparent loss rate constants ranging from 0.21 to 0.78day−1. These findings are used to produce an empirical scheme describing bromocarbon cycling in natural waters which is validated against measured concentration data from the RaTS site. The detailed process information and schemes presented provide a major step forward towards the development of biogeochemical modules that could be coupled to ecosystem models. These could then be used to predict how sea-to-air biogenic bromine emissions will change under future scenarios.

CO2 Capture Processes: Novel Approach to Benchmarking and Evaluation of Improvement Potentials

A consistent and transparent benchmarking methodology is required for comparison and ranking of different technologies for CO2 capture, and also for evaluation of different means of integrating novel capture process units into power cycles. A novel benchmarking methodology is presented in this work where the difference between the thermodynamic maximum and the technology limited efficiencies quantifies the theoretical improvement potential and is a benchmark for the process. Additionally, the source(s) of this difference in efficiency can point to possible future directions for technology development. . The benchmarking methodology is applied to the three CO2 capture routes – post-combustion, pre-combustion and oxy-combustion – using simplifying assumptions and with fuels of varying HC ratios. The first step of the benchmarking methodology is defining ideal reversible processes for CO2 capture with no detailed process information. This enables determination of a consistent thermodynamic maximum efficiency of processes. Results show that apart from methane as fuel, where post-combustion has the lowest thermodynamics limited efficiency penalty, pre-combustion process routes have the lowest thermodynamics limited efficiency penalty, followed by post-combustion and then oxy-combustion. These results should not be seen as an attempt to rank the different capture routes, but rather as the thermodynamic limit for technological improvements.

A Framework for Layout-Dependent STI Stress Analysis and Stress-Aware Circuit Optimization

With the continuous shrinking of the feature size, the effect of stress on the performance of the IC device and circuit can no longer be ignored. In fact, stress engineering is becoming more and more widely used today in advanced IC manufacture processes to improve device performance. Different from the intentionally introduced stresses to improve circuit performance, the shallow-trench-isolation (STI) stress, which is exerted by STI wells on the active area of devices, is a by-product of the fabrication process and has increasingly significant impact on the circuit behavior. This paper proposes a complete flow to characterize the influence of STI stress on the performance of RF/analog circuits by considering detailed layout and process information. An accurate and efficient finite-element method-based stress simulator has been developed to extract stress distribution from layouts of IC designs. The existing MOSFET model is also enhanced to capture the effects of stress on mobility, threshold voltage. With the enhanced model, we are able to study the influence of layout-dependent STI stress on the performance of real circuits and establish corresponding optimization strategies. The proposed flow has been applied to a series of RF/analog IC designs based on a 90-nm CMOS technology.

Exposure assessment of tetrafluoroethylene and ammonium perfluorooctanoate 1951–2002

To develop a method to reconstruct exposure to tetrafluoroethylene (TFE) and ammonium perfluorooctanoate (APFO) in plants producing polytetrafluoroethylene (PTFE) in the absence of suitable objective measurements. These data were used to inform an epidemiological study being carried out to investigate possible risks in workers employed in the manufacture of PTFE and to study trends in exposure over time. For each plant, detailed descriptions of all occupational titles, including tasks and changes over time, were obtained during semi-structured interviews with key plant personnel. A semi-quantitative assessment method was used to assess inhalation exposure to TFE and inhalation plus dermal exposure to APFO. Temporal trends in exposure to TFE and APFO were investigated. In each plant the highest exposures for both TFE and APFO occurred in the polymerisation area. Due to the introduction of control measures, increasing process automation and other improvements, exposures generally decreased over time. In the polymerisation area, the annual decline in exposure to TFE varied by plant from 3.8 to 5.7% and for APFO from 2.2 to 5.5%. A simple method for assessing exposure was developed which used detailed process information and job descriptions to estimate average annual TFE and APFO exposure on an arbitrary semi-quantitative scale. These semi-quantitative estimates are sufficient to identify relative differences in exposure for the epidemiological study and should good data become available, they could be used to provide quantitative estimates for all plants across the whole period of operation.

Design and Process Implementation for Embedding Components

Although the printed circuit has traditionally served as the platform for mounting and interconnecting active and passive components on the outer surfaces, companies attempting to improve product functionality and minimize space are now considering embedding a broad range of these components within the circuit structure. Both uncased active and passive component elements are candidates for embedding but the decision to embed components within the multilayer circuit structure must be made early in the design process. Some components are easy candidates for integrating into the substrate while other may involve more complex processes and will be difficult to rationalize. Although many of the discrete passive and active devices may remain mounted on the outer surfaces of the multilayer board, embedding passive components and one or more silicon based semiconductor elements within the inner layers of the subsrate will enable greater utilization of the circuit board structure. Benefits can include improved performance. For example, by embedding the uncased semiconductor element on an inner layer of the circuit vertically in-line with a related semiconductor package mounted on the outer surface, the conductor interface can be minimized. The close coupling of semiconductor elements will significantly reduces resistance, inductance and contributes to increasing signal speed. This paper includes design guidelines, material selection and the use of micro-via build-up termination methodology for embedding and interconnecting miniature passive components; resistor, capacitor, inductor, discrete transistor elements and thinned semiconductor elements. Detailed process information and examples will be included to enable the product designer and manufacturing specialist to have a clear understanding of the principles for embedding and interconnecting components in an organic multilayer circuit board structure

Sustainability indicators for decision-making and optimisation in the process industry: The case of the petrochemical industry

Abstract This paper brings into focus simple sustainable development indicators to be used in the planning of the petrochemical industry. The indicators cover the three aspects of sustainability—environmental, economic and safety. The indicators serve as tools that can guide companies in assessing their performance in decision-making and planning for the future. These simple indicators of sustainable development are especially helpful in planning large projects or during the earliest stages of planning when most detailed process information is still lacking. These sustainability indicators were used as objectives for a mixed-integer optimisation model to plan the development of a typical petrochemical industry, and were found to be very useful in identifying a balanced petrochemical network. A simple Monte Carlo simulation showed that the model is able to accommodate variations in prices and demand.