Emission Minimization of a Two-Stage Sour Water Stripping Unit Using Surrogate Models for Improving Heat Duty Control

Sour water are aqueous waste streams from oil refining operations, heavily contaminated with hydrogen sulfide and ammonia, which need to be stripped before reuse or disposal, avoiding damages to process and environment. Two-stage sour water stripper units are the most common technology to treat sour water for hydrogen sulfide and ammonia separation to produce reusable water and send these species respectively to Claus and ammonia plants. The first stage of a two-stage sour water unit is responsible for properly splitting hydrogen sulfide and ammonia. This work uses surrogate models to predict the limiting point of hydrogen sulfide separation in the first stage of a sour water unit, allowing more efficient heat duty control strategies to achieve the difficult split of hydrogen sulfide and ammonia and simultaneously lowering heat consumption. Failure of compliance to this limit results in unspecified stripped gas from the first stage, impeding it to directed to Claus plant, entailing loss of sulfur production and higher load of pollutant emissions from flared gases. Therefore, a precise surrogate predictor was developed to dynamically define a quasi-optimum set-point to the controller of the first stage reboiler duty based on dynamic disturbances – the first stage input factors to the surrogate model, such as hydrogen sulfide and ammonia contents of the sour water. The new control policy outperformed the traditional first stage ratio control in terms of stripped gas composition and plant stability

Assessment of Methods to Pretreat Microalgal Biomass for Enhanced Biogas Production

In anaerobic digestion of microalgae, the intracellular material may remain intact due to the non-ruptured membrane and/or cell wall, reducing the methane yield. Therefore, different pretreatment methods were evaluated for the solubilization of microalgae Scenedesmus sp. The anaerobic digestion of biomass hydrolyzed at 150 °C for 60 min with sulfuric acid 0.1% v/v showed higher methane yield (204-316 mL methane/g volatile solids applied) compared to raw biomass (104-163 mL methane/g volatile solids applied). The replacement of sulfuric acid with carbonic acid (by bubbling carbon dioxide up to pH 2.0) provided results similar to those obtained with sulfuric acid, reaching solubilization of 41.6% of the biomass. This result shows that part of the flue gas (containing carbon dioxide and other acid gases as well as high temperatures) may be used for the hydrolysis of the residual biomass from microalgae, thus lowering operational costs (e.g., energy consumption and chemical input)

A Monte Carlo Methodology for Environmental Assessment Applied toOffshore Processing of Natural Gas with High Carbon Dioxide Content

Offshore production of oil and natural gas with high carbon dioxide content and high gas-to-oil ratio entail stringent processing conditions that require innovations and first-of-a-kind designs, which bear uncertainties derived from the scarcity of commercial-scale projects, hindering to move along technology learning curves. Consequently, unpredicted scenarios and unachieved specifications cause economic and environmental losses. Such uncertainties force offshore plants to be designed under stochastic factors seeking best statistical performance. The Monte Carlo Method is suitable to such finality. This work proposes a computer-aided engineering framework ‘MCAnalysis’ automatically applying a probabilistic environmental assessment of offshore gas processing. ‘MCAnalysis’ integrates HYSYS simulator with ‘Waste Reduction Algorithm’ to assess potential environmental impacts, whose most relevant categories were identified via Principal Component Analysis. An offshore plant processing natural gas with high carbon dioxide content was submitted to probabilistic raw gas flow rate under two scenarios of carbon dioxide content. The higher carbon dioxide content scenario presented the highest probabilistic potential environmental impacts, being the atmospheric category the most relevant

IPC02-27145 A TIME SERIES APPROACH FOR PIPE NETWORK SIMULATION

ABSTRACT We applied time series predicting tools for the simulation of the temporal behavior of large pipeline networks submitted to timely changing inputs. The inputs may consist of a set of specified flow rates at client or supply nodes, while the outputs are another set of nodal pressures and internal flow rates. According to the topology, size, age and history of the network, the continuous generation of phenomenological dynamic simulations may be impossible, imprecise or numerically expensive, demanding thus alternative approaches. Our methodology is particularly oriented to this kind of demand. From recorded network past data covering relevant history of inputs and selected outputs, ARX-MIMO predictors are built with identification methods and launched for continuous estimation of the network outputs one time step ahead. Results are precise enough for engineering, training and monitoring applications