Ammonium and Phosphate Recovery in a Three Chambered Microbial Electrolysis Cell: Towards Obtaining Struvite from Livestock Manure

Ammonia and phosphate, which are present in large quantities in waste streams such as livestock manure, are key compounds in fertilization activities. Their recovery will help close natural cycles and take a step forward in the framework of a circular economy. In this work, a lab-scale three-chambered microbial electrolysis cell (MEC) has been operated in continuous mode for the recovery of ammonia and phosphate from digested pig slurry in order to obtain a nutrient concentrated solution as a potential source of fertilizer (struvite). The maximum average removal efficiencies for ammonium and phosphate were 20% ± 4% and 36% ± 10%, respectively. The pH of the recovered solution was below 7, avoiding salt precipitation in the reactor. According to Visual MINTEQ software modelling, an increase of pH value to 8 outside the reactor would be enough to recover most of the potential struvite (0.21 mmol L−1 d−1), while the addition of up to 0.2 mM of magnesium to the nutrient recovered solution would enhance struvite production from 5.6 to 17.7 mM. The application of three-chambered MECs to the recovery of nutrients from high strength wastewater is a promising technology to avoid ammonia production through industrial processes or phosphate mineral extraction and close nutrient natural cycles.

ModelSet: a dataset for machine learning in model-driven engineering

The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io.

Improving Production Forecasting in a Mature Onshore Oilfield Brownfield in Southern Trinidad by Making Use of Software Modeling

In Trinidad's mature onshore oilfields, operators have traditionally forecasted the initial production rates back calculated from decline models. These rates, then reduced annually by a predetermined decline model has been used to evaluate financial feasibility. This method does not make use of the reservoir pressure. This paper demonstrates how software modelling, utilizing the reservoir pressure can reasonably forecast the performance of low rate oil producers and alert the operator of the need for artificial lift from the inception of the production cycle. The objectives of the project were to determine remaining recoverable reserves, evaluate the potential for redevelopment (workovers and infill drilling) and to demonstrate that software modeling can be used to forecast production for an oil reservoir in a mature onshore oilfield in Southern Trinidad. Petroleum Experts Integrated Production Modeling (IPM) software suite was used for building all models. A comparison of the production forecasted by software modelling and the traditional method of forecasting initial production rates by back calculating from decline models was also undertaken. Using the available data and net oilsand maps, the fault block bulk volumes, oil in place and the remaining reserves were determined. These results were then used to identify fault blocks with potential workover well candidates and infill well locations. Research of well files and well logs were used in evaluating zones for potential recompletions, reperforation or perforation of additional footage for production. Forecasting and comparison of the initial production rates and ultimate cumulative production for the proposed infill wells and recompletions using the traditional IP/Decline model method and computer modeling was then performed. Form the data available, it was determined there were four blocks with remaining reserves that could be successfully recovered. The recovery methods proposed included the workover of two existing wells and drilling of two infill wells. Initial production rates and ultimate production volumes obtained by modeling of workover and new well performance had reasonably close agreement with those obtained by the traditional IP/Decline models. The results of the modeling, however indicated that all the wells required the use of pumping mechanisms (sucker rod/beam pumps) to sustain production over a ten-year period. The need for this important production mechanism would not have been realized from the IP/Decline method. An important distinction is that the modelling makes direct use of the reservoir pressure, whereas the IP/Decline model does not.

Predictive Modelling and Technical Design Application into Effective Casing Wear Operational Management Plan

Predicting casing wear has often been regarded as an empirical art as there are many influencing factors, including but not limited to the sizes and grades of the drill pipe and casing, type of hardbanding, drilling fluid properties, rate of penetration, trajectory and formation properties. Formations present in offshore Western Australia often contain loose and friable sands which produce highly abrasive cuttings which, when suspended and circulated in drilling fluid, are known to exacerbate casing wear. Casing wear is considerably worse in deviated and multilateral (ML) wells; Woodside's experience drilling ML wells has involved costly non-productive time (NPT) due to the subsequent requirement for remedial tieback systems to maintain well integrity. In 2018 and 2019 three tri-lateral wells were drilled as part of the larger Greater Enfield Project drilling campaign. Each of the multilateral wells were progressively longer and more challenging with regard to casing wear. Previous experience on nearby wells in analogous fields identified casing wear as a significant risk for the project. Further to this, an opportunity was identified to design the longest tri-lateral well as a quad-lateral well, which would allow increased recovery if reservoir quality was poorer than expected. The Drilling and Completion Engineering team were challenged with proving that casing wear could be effectively evaluated and managed during operations to allow a quad-lateral well design if required. Several key areas were investigated in order to effectively manage casing wear. These included: Assessment and measurement of casing manufacturing tolerances;Predictive casing wear modelling using well offsets in conjunction with casing wear software;Casing connection finite element analysis and mechanical hardbanding testing;Full length ultra-sonic testing of casing for wall thickness benchmarking;Hardbanding management plan (which formed part of the overall drill pipe fatigue management plan);Casing wear management plan based on well offsets and casing wear software modelling results, including additional controls such as 'krev' and swarf monitoring;Planning and execution of casing wear logging;Post well evaluation. The casing wear operational plan was effective in monitoring and limiting the amount of wear. It provided confidence to the management team that successful execution of a quad-lateral well was feasible. This paper will describe the steps taken to minimise casing wear, discuss comparisons between the predicted wear and the actual measured casing wear, and provide a recommended workflow for predicting casing wear in future wells where casing wear is a critical factor.

Disconnect Tool Successfully and Efficiently Cements Electromagnetic Gauge Assembly in Place to Provide Key Reservoir Data

An operator developing a deepwater field in the eastern Mediterranean required to monitor pressures in an upper sand section while producing from the main lower sands. If communication existed between the two zones, a planned late-life workover could be eliminated, reducing development cost. Gauges placed across the upper sands in a pilot hole would transmit pressure data to the production bore using electromagnetic (EM) transmission technology. Ensuring isolation of these gauges by cement was identified as critical in enabling effective EM data transmission and therefore a great deal of focus was placed on the design of the cement job. To perform the operation in as efficient manner as possible a tailored assembly was developed consisting of electronic gauges and EM relays isolated by open hole packers, along with a cementing assembly to allow cementation of the upper part of the string which included an EM receiver and relay in place. The cementing assembly consisted of a frac sleeve to allow the completion to be run and cemented in place, and a disconnect tool for the drill string to be disconnected in one run. Once disconnected from the completion, the abandonment of the pilot hole could continue without a trip out of the hole, saving significant time and costs to the operator. The cementjob design was tailored and verified by lab testing and software modelling to meet the objectives of the job and the unique challenge associated with the placement method proposed. Once the completion was installed in the production bore, communication between the gauges through the EM transmission system was confirmed and monitored during the subsequent well cleanup. The communication test verified annular isolation and system operability. Furthermore, upper and lower zonal isolation was proven by monitoring the gauge data in an interference test when flowing another well.

The Business Transformation and Enterprise Architecture Framework The Applied Holistic Mathematical Model’s Persistence Concept (AHMMPC)

This article proposes a cross domain Applied Holistic Mathematical Model (AHMM) that is the result of a lifetime long research on various types of transformations, applied mathematics, software modelling, heuristic brain-like algorithms, business engineering, financial analysis and global system architecture. This ultimate research is based on an authentic and proprietary mixed research method that is supported by an underlining mainly qualitative holistic neural networks algorithms [1]. The proposed HMM formalism attempts to mimic and simulate some functions of the human brain, which uses empirical processes that are mainly based on the beam-search, like heuristic decision-making process that uses a persistence concept.

Model Driven Software Engineering Meta-Workbenches: An XTools Approach

Model Driven Software Engineering aims to provide a quality assured process for designing and generating software. Modelling frameworks that offer technologies for domain specific language and associated tool construction are called language workbenches. Since modelling is itself a domain, there are benefits to applying a workbenchbased approach to the construction of modelling languages and tools. Such a framework is a meta-modelling tool and those that can generate themselves are reflective metatools. This article reviews the current state of the art for modelling tools and proposes a set of reflective meta-modelling tool requirements. The XTools framework has been designed as a reflective meta-tool and is used as a benchmark.

Simulating the AM Production Facility: A Configurable Software Tool for Strategic Facility-Level Planning

Additive Manufacturing (AM) — one of several core digital technologies in “Industry 4.0” — is increasingly being deployed in industrial-scale contexts. The successful serial production of end-use polymer and metal components has demonstrated the possibility of AM as a primary production process in several applications. However, one of the principal challenges to greater adoption is a lack of organizational mastery over AM’s implementation in production contexts, and, more specifically, the absence of clear decision-making tools to facilitate exploration of implementation scenarios. To this end, this work proposes the use of a discrete-event simulation-based software modelling tool to investigate the influences of different facility-level planning decisions on techno-economic characteristics of serial production by AM. By changing key parameters, this tool enables users to observe variation in part cost, identify the contributions of individual system elements to part cost, and assess overall system throughput. The tool enables users to identify locally optimal solutions and make corresponding planning decisions, and to explore limiting cases of cost and lead time. In conclusion, we identify the limitations in the current modeling approach, and propose additional directions for future study.