Applying Industrial Symbiosis to Smallholder Farms

The study of industrial symbiosis (IS) has largely focused on the exchange of energy and materials among industrial processes in an effort to increase value and reduce environmental impact. Agricultural systems, particularly those located in developing countries, can benefit from the principles of IS. Relatively few studies have analyzed the potential benefits of integrated material and energy flows in smallholder farming, even though these systems are considered essential to the world's food supply and poverty reduction. Although the concepts can be applied to virtually any system, the study of industrial symbiosis has traditionally focused on industrialized systems in developed countries. The research presented here applies the principles of IS to smallholder farms using optimization techniques to maximize farm output while minimizing wastes. Our research links IS to the growing field of integrated farming research (IFR), which seeks to create new technologies that increase the production of farms by viewing the farm as a system. Bridging these fields enriches the potential for robust research outcomes in both areas and fills a current knowledge gap. IS benefits from exploring new applications and increasing its penetration into the developing world. IFR benefits from established IS tools to create alternate pathways for increased output based on symbiotic relationships. A small farming system in Liberia, West Africa, is used as a case study. System integration of individual unit processes shows increased productivity and decreased waste. The results of this analysis indicate that there are unrealized opportunities for IS in developing countries, and integration of IS techniques into smallholder farming operations has the potential for impacting sustainable development.     

Functional nodes in dynamic neural networks for bioprocess modelling

This contribution presents a novel method for the direct integration of a-priori knowledge in a neural network and its application for the online determination of a secondary metabolite during industrial yeast fermentation. Hereby, existing system knowledge is integrated in an artificial neural network (ANN) by means of 'functional nodes'. A generalized backpropagation algorithm is presented. For illustration, a set of ordinary differential equations describing the diacetyl formation and degradation during the cultivation is incorporated in a functional node and integrated in a dynamic feedforward neural network in a hybrid manner. The results show that a hybrid modelling approach exploiting available a-priori knowledge and experimental data can considerably outperform a pure data-based modelling approach with respect to robustness, generalization and necessary amount of training data. The number of training sets were decreased by 50%, obtaining the same accuracy as in a conventional approach. All incorrect decisions, according to defined cost criteria obtained with the conventional ANN, were avoided.     

An on-line advisory control system for the lactic acid fermentation process

A fuzzy expert system was developed for online diagnosing and controlling of bioprocesses. The system was constructed in object-oriented Smalltalk/V for diagnosing and controlling of bioprocesses. Lactic acid fermentation with an industrial strain ofLactobacillus casei was chosen as the model system. The performance of the fuzzy expert system and the knowledge base utilizing experts' knowledge and several facts obtained from the experiments were successfully validated with on-line fermentations. The fuzzy expert system could diagnose a fault on-line and give reasonable advice to the process operator. In order to achieve the diagnosing faculty, a database, a knowledge base, and both backward and forward chaining procedures were implemented employing the object-oriented programming environment. A defuzzifier was implemented in the system to achieve on-line control. In order to realize a decision-making system with a human operator and a fuzzy expert system, a new control strategy namedAdvice was also introduced. Several cultivations were carried out in order to collect knowledge on the effects concerned with inoculum properties to the process and to construct a database including standard time-course profiles. The performance of the fuzzy expert control system was successfully tested with on-line experiments.Visiting scientist from HUT at RIKEN     

Autonomous recovery in componentized Internet applications

In this paper we show how to reduce downtime of J2EE applications by rapidly and automatically recovering from transient and intermittent software failures, without requiring application modifications. Our prototype combines three application-agnostic techniques: macroanalysis for fault detection and localization, microrebooting for rapid recovery, and external management of recovery actions. The individual techniques are autonomous and work across a wide range of componentized Internet applications, making them well-suited to the rapidly changing software of Internet services. The proposed framework has been integrated with JBoss, an open-source J2EE application server. Our prototype provides an execution platform that can automatically recover J2EE applications within seconds of the manifestation of a fault. Our system can provide a subset of a system's active end users with the illusion of continuous uptime, in spite of failures occurring behind the scenes, even when there is no functional redundancy in the system.     

Similarity Ratio Analysis for Early Stage Fault Detection with Optical Emission Spectrometer in Plasma Etching Process

A Similarity Ratio Analysis (SRA) method is proposed for early-stage Fault Detection (FD) in plasma etching processes using real-time Optical Emission Spectrometer (OES) data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR) statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A), takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes.     

Automated purification and suspension array detection of 16S rRNA from soil and sediment extracts by using tunable surface microparticles

Autonomous, field-deployable molecular detection systems require seamless integration of complex biochemical solutions and physical or mechanical processing steps. In an attempt to simplify the fluidic requirements for integrated biodetection systems, we used tunable surface microparticles both as an rRNA affinity purification resin in a renewable microcolumn sample preparation system and as the sensor surface in a flow cytometer detector. The tunable surface detection limits in both low- and high-salt buffers were 1 ng of total RNA ( approximately 10(4) cell equivalents) in 15-min test tube hybridizations and 10 ng of total RNA ( approximately 10(5) cell equivalents) in hybridizations with the automated system (30-s contact time). RNA fragmentation was essential for achieving tunable surface suspension array specificity. Chaperone probes reduced but did not completely eliminate cross-hybridization, even with probes sharing <50% identity to target sequences. Nonpurified environmental extracts did not irreparably affect our ability to classify color-coded microparticles, but residual environmental constituents significantly quenched the Alexa-532 reporter fluor. Modulating surface charge did not influence the interaction of soluble environmental contaminants with conjugated beads. The automated system greatly reduced the effects of fluorescence quenching, especially in the soil background. The automated system was as efficacious as manual methods for simultaneous sample purification, hybridization, and washing prior to flow cytometry detection. The implications of unexpected target cross-hybridization and fluorescence quenching are discussed relative to the design and implementation of an integrated microbial monitoring system.     

A diagnostic expert system

The introduction of new technologies in the field of electronics has influenced the development of technical equipment over the last few years. The progressive miniaturization of integrated circuits makes possible an expansion of the spectrum of functions offered by this equipment. This also applies to medical technology. These more complex units call for new methods of fault detection and diagnosis. In addition to analytical redundancy, tools developed by the artificial intelligence research community, such as expert systems, are becoming more and more important for fault diagnosis. On the basis of a realized diagnosis expert system the possibilities as well as the limits of such system are discussed. Also, possible future developments of artificial intelligence, like machine learning, are considered.     

A portable generic DNA bioassay system based on in situ oligonucleotide synthesis and hybridization detection

In this study, we present a portable and generic DNA bioassay system based on in situ oligonucleotide synthesis followed by hybridization based detection. The system include two main parts, an oligonucleotide synthesizer and a fluorescence detection system. The oligonucleotide synthesizer is based on microfluidic technology and capable of synthesizing any desired oligonucleotide which can be either used as a primer for PCR based detection (external) or a probe for hybridization based detection (integrated) of a target DNA analyte. The oligonucleotide sequence can be remotely sent to the system. The integrated fluorescence detection system is based on a photodiode to detect Texas Red fluorophore as low as 0.5 fmol. The complete system, integrating the oligonucleotide synthesizer and fluorescence detection system, was successfully used to distinguish DNA from two different bacteria strains. The presented generic portable instrument has the potential to detect any desired DNA target sequence in the field. Potential applications are for homeland security and fast responses to emerging bio-threats.     

Internet-Based Integrated Environmental Assessment Using Ontologies to Share Computational Models

New advances in Internet technologies and computer modeling provide opportunities for collaborative systems to support research and development in the field of industrial ecology. In particular, new information technologies such as semantic search engines based on ontologies could help researchers to link fragments of knowledge generated at research centers from around the world. Using a storyline of four imaginary researchers who hope to find collaborators in order to develop their research findings, we illustrate two levels of a four-level architecture for an Internet-based knowledge integration and collaboration environment for integrated environmental assessment. The foundation of the proposed architecture is a belief that computational models are an effective medium for conveying expert knowledge of various phenomena. Drawing from this premise, the first level of the architecture stands on a base of computational models that in some way represent the expert knowledge of the model builder. At the second level, we provide markup and interface definition tools to describe the type of knowledge contained in each model, together with the types of information services that can be provided.
The results of research at these two levels of an Internet-based knowledge integration environment for integrated environmental assessment in industrial ecology are presented in this article. Our work on the third level of model searching and matching and the fourth level of parametric model integration and solving will be presented in subsequent articles.     

Fault detection and diagnosis in an industrial fed-batch cell culture process

A flexible process monitoring method was applied to industrial pilot plant cell culture data for the purpose of fault detection and diagnosis. Data from 23 batches, 20 normal operating conditions (NOC) and three abnormal, were available. A principal component analysis (PCA) model was constructed from 19 NOC batches, and the remaining NOC batch was used for model validation. Subsequently, the model was used to successfully detect (both offline and online) abnormal process conditions and to diagnose the root causes. This research demonstrates that data from a relatively small number of batches (approximately 20) can still be used to monitor for a wide range of process faults.     

Characterization and validation of a bioluminescent bioreporter for the direct detection of Escherichia coli

A two-component bacteriophage-based bioluminescent reporter system was developed for the detection of Escherichia coli in environmental samples. The bioreporter system consists of a luxI integrated lambda bacteriophage and a lux-based bioluminescent reporter cell that responds to the infection event through acyl-homoserine lactone (AHL) mediated quorum sensing and bioluminescent signal stimulation. This work addresses the ability of the bioreporter system to detect and quantify the target pathogen in response to two analytical challenges: (1) detection of target cells in the presence of lactonase-producing non-target organisms that could interrupt AHL signal transduction, and (2) detection of sub-lethally injured or physiologically stressed target cells. The bioreporter system was able to autonomously respond to lambda phage infection events with a target host E. coli at 1x10(8) cfu/mL against a background of lactonase-producing Arthrobacter globiformis at cell densities ranging from 1 to 1x10(8) cfu/mL. E. coli target cells stressed by carbon-limitation for 2 weeks (i.e., starvation) or exposure to iodine for 1 week at 2 and 20 ppm (i.e., disinfection) yield a reduced, but detectable, biosensor response. Conversely, short-term iodine exposure produces a significant increase in bioreporter response within the first 24 h. The signal response and limit of detection for the two-component bioreporter system were affected by the physiology and environment of the target, but the bioreporter maintained target specificity demonstrating its potential application for remote sensing of pathogens.     

Industrial wastewater treatment in internal circulation bioreactor followed by wetlands containing emergent plants and algae

Wastewater treatment based on ecological principles is a low cost and highly desirable solution for the developing countries like Pakistan. The present study evaluated the effectiveness of biological treatment systems including Internal Circulation (IC) anaerobic bioreactor and constructed wetlands (CWs) containing macrophytes and mixed algal cultures for industrial wastewater treatment. The IC bioreactor reduced COD (52%), turbidity (89%), EC (24%) of the industrial wastewater. However, the effluents of IC bioreactor did not comply with National Environmental Quality Standards (NEQS) of Pakistan. Post-treatment of IC bioreactor effluents was accomplished in CW containing macrophytes (Arundo donax and Eichhornia crassipes) and mixed algal culture. The CWs planted with macrophytes lowered the concentrations of COD (89%) and turbidity (99%). CWs with algal biomass were not effective in further polishing the effluent. Inhibition of algal biomass growth was observed due to physicochemical characteristics of wastewater. The integrated treatment system consisting of IC bioreactor and macrophytes was found more suitable option for industrial wastewater treatment.     

Performance evaluation of fault detection methods for wastewater treatment processes

Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non-detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long-term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods.     

A novel Real Time micro PCR based Point-of-Care device for Salmonella detection in human clinical samples

Our POC (Point of Care) device is intended to be a diagnostic tool for routine use in the clinical sector. The validation of the whole procedure, including bacterial genomic DNA isolation and the Real Time detection of Salmonella spp., was conducted on 29 clinical stool samples that had been diagnosed with Salmonella spp. by a routine culture technique. The entire process was achieved in a single microfluidic chip within 35 min. In comparison to the culture reference method that is used in the clinical laboratories, this new device performed well in regards to the analytical parameters of sensitivity, specificity and accuracy. Therefore, the POC device reported in this study proved to be very appropriate for the fully integrated analysis system. To the best of our knowledge, this is the first work to report the sample preparation and followed by Real Time PCR (Polymerase Chain Reaction) on a single 2.5 μl chamber chip for the detection of Salmonella spp. bacteria in stool samples.     

Study of integrated risk regionalisation method for soil contamination in industrial and mining area

Industrial and mining areas have been included as key governance plots of China’s soil management and protection. In this study, we developed an assessment method for soil integrated risk in industrial and mining areas, with a comprehensive consideration of pollution risk, pollution sources and receptors of soil risk. The method consists of four parts, including soil risk assessment, vulnerability assessment of soil risk receptors, risk level assessment of pollution sources and integrated risk assessment. Using this method, we could produce a complete soil risk regionalisation map that presents the total factor risk level of soil contamination of industrial and mining areas in ArcGIS. This study also took a typical industrial and mining area in China as a case and quantitatively and spatially assessed the integrated soil contamination risk. Results showed: (1) the integrated risk of the study area ranged from moderate level to high level; (2) the risk of pollution sources in the study area ranged from moderate level to high level; (3) vulnerability of soil risk receptors in streets was lower than that in the towns.     

Intercompany Energy Integration

Reusing heat through process integration in heat exchanger networks has long been a key measure for increasing energy efficiency in energy‐intensive industries. Thermal pinch analysis is commonly used for a systematic matching of process streams and thus planning of optimal process integration in large chemical plants. The possible savings increase with the amount of heat and the number of integrated process streams. Therefore co‐ siting of several companies in a symbiotic network opens new opportunities for process integration even in small and medium‐size enterprises (SMEs), but also introduces new challenges. Thermal pinch analysis is extended here to account for piping distances and fluctuations and limited availability of energy flows by adding additional costs for the piping system and a backup utility system in the optimization function. Cooperative game theory is proposed to derive a sharing of savings between the partners of the industrial symbiosis that is optimal for each partner and should prevent partners from leaving the network because of higher benefits in a subgroup or alone. It is argued that knowledge about the optimality of a network for each partner creates trust between the partners that is a necessary base for the long‐term commitment needed in industrial symbioses. An exemplary symbiotic network combining the production of pulp and woody biomass energy carriers is used to illustrate the proposed approaches.     

Parallel hybrid modeling methods for a full-scale cokes wastewater treatment plant

Parallel hybrid modeling methods are applied to a full-scale cokes wastewater treatment plant. Within the hybrid model structure, a mechanistic model specifies the basic dynamics of the relevant process and a non-parametric model compensates for the inaccuracy of the mechanistic model. First, a simplified mechanistic model is developed based on Activated Sludge Model No. 1 and the specific process knowledge of the cokes wastewater treatment process. Then, the mechanistic model is combined with five different non-parametric models--feedforward back-propagation neural network, radial basis function network, linear partial least squares (PLS), quadratic PLS and neural network PLS (NNPLS)--in parallel configuration. These models are identified with the same data obtained from the plant operation to predict dynamic behavior of the process. The performance of each parallel hybrid model is compared based on their ease of model building, prediction accuracy and interpretability. For this application, the parallel hybrid model with NNPLS as non-parametric model gives better performance than other parallel hybrid models. In addition, the NNPLS model is used to analyze the behavior of the operation data in the reduced space and allows for fault detection and isolation.     

Evaluation of the teaching strategy of cardiovascular system in a problem-based curriculum: student perception

It is generally acknowledged that an integrated approach to teaching cardiovascular system (CVS) is clinically relevant. However, very little attention has been paid with respect to student perception of teaching CVS in an integrated problem-based curriculum. A questionnaire on the feedback and perception of medical students (n = 60) to their learning experience of CVS exposed early in the problem-based integrated curriculum at the Arabian Gulf University (AGU) was used. The average percentage scores of positive student responses to items related to knowledge was 62.7%, to integration was 87.3%, and to skills was 77.1%. A significant positive correlation was observed among skills and knowledge (r = 0.408, P = 0.002), skills and integration (r = 0.506, P < 0.000), and integration and knowledge (r = 0.294, P = 0.028). The lowest individual percentage score related to knowledge items was given to the role of resource sessions in understanding difficult concepts (32.7%). Interestingly, 90.7% of the students were aware of the presence of gaps in their knowledge. On the other hand, 92.7% of students expressed their satisfaction with the study experience of CVS in the integrated problem-based approach. These results indicate that students overall achieved satisfactory learning outcome during the study of CVS in the problem-based integrated curriculum at AGU. The study also points out issues where improvement and fine tuning of the educational system can take place.     

Flash detection/identification of pathogens, bacterial spores and bioterrorism agent biomarkers from clinical and environmental matrices.

We propose to develop an integrated rapid, semiportable, prototype point microbial detection/identification system for clinical specimens that is also capable of differentiating microbial bioterrorism attacks from threats or hoaxes by defining the pathogen. The system utilizes "flash" extraction/analytical system capable of detection/identification of microbes from environmental and clinical matrices. The system couples demonstrated technologies to provide quantitative analysis of lipid biomarkers of microbes including spores in a system with near-single cell (amol/microl) sensitivity. Tandem mass spectrometry increases specificity by providing the molecular structure of neutral lipids, phospholipids, and derivatized spore-specific bacterial biomarker, 2,6-dipicolinic acid (DPA) as well as the lipopolysaccharide-amide-linked hydroxy-fatty acids (LPS-ALHFA) of Gram-negative bacteria. The extraction should take about an hour for each sample but multiple samples can be processed simultaneously.