Applications of capillary electrophoresis and mass spectrometry for the analysis of thiosalts

Thiosalt species are unstable, partially oxidized sulfur oxyanions formed in sulfur-rich environments but also during the flotation and milling of sulfidic minerals especially those containing pyrite (FeS₂) and pyrrhotite (Fe₍₁₋ₓ₎S, x = 0 to 0.2). Detecting and quantifying the major thiosalt species such as sulfate (SO₄²⁻), thiosulfate (S₂O₃²⁻), trithionate (S₃O₆²⁻), tetrathionate (S₄O₆²⁻) and higher polythionates (SₓO₆²⁻, where 3 ≤ x ≤ 10) in the milling process and in the treated tailings is important to understand how thiosalts are generated and provides insight into potential treatment. As these species are unstable, a fast and reliable analytical technique is required for their analysis. Three capillary zone electrophoresis (CZE) methods using indirect UV-vis detection were developed for the simultaneous separation and determination of five thiosalt anions: SO₄²⁻, S₂O₃²⁻, S₃O₆²⁻, S₄O₆²⁻ and S₅O₆²⁻. Both univariate and multivariate experimental design approaches were used to optimize the most critical factors (background electrolyte (BGE) and instrumental conditions) to achieve fast separation and quantitative analysis of the thiosalt species. The mathematically predicted responses for the multivariate experiments were in good agreement with the experimental results. Limits of detection (LODs) (S/N = 3) for the methods were between 0.09 and 0.34 μg/mL without a sample stacking technique and nearly four-fold increase in LODs with the application of field-amplified sample stacking. As direct analysis of thiosalts by mass spectrometry (MS) is limited by their low m/z values and detection in negative mode electrospray ionization (ESI), which is typically less sensitive than positive ESI, imidazolium-based (IP-L-Imid and IP-T-Imid) and phosphonium-based (IP-T-Phos) tricationic ion-pairing reagents were used to form stable high mass ions non-covalent +1 ion-pairs with these species for ESI-MS analysis and the association constants (Kassoc) determined for these ion-pairs. Kassoc values were between 6.85 × 10² M⁻¹ and 3.56 × 10⁵ M⁻¹ with the linear IP-L-Imid; 1.89 ×10³ M⁻¹ and 1.05 × 10⁵ M⁻¹ with the trigonal IP-T-Imid ion-pairs; and 7.51×10² M⁻¹ and 4.91× 10⁴ M⁻¹ with the trigonal IP-T-Phos ion-pairs. The highest formation constants were obtained for S₃O₆²⁻ and the imidazolium-based linear ion-pairing reagent (IP-L-Imid), whereas the lowest were for IP-L-Imid: SO₄²⁻ ion-pair.

An application of evolutionary algorithms for WAG optimisation in the Norne Field

Water-alternating-gas (WAG) is an enhanced oil recovery method combining the improved macroscopic sweep of water flooding with the improved microscopic displacement of gas injection. The optimal design of the WAG parameters is usually based on numerical reservoir simulation via trial and error, limited by the reservoir engineer’s availability. Employing optimisation techniques can guide the simulation runs and reduce the number of function evaluations. In this study, robust evolutionary algorithms are utilized to optimise hydrocarbon WAG performance in the E-segment of the Norne field. The first objective function is selected to be the net present value (NPV) and two global semi-random search strategies, a genetic algorithm (GA) and particle swarm optimisation (PSO) are tested on different case studies with different numbers of controlling variables which are sampled from the set of water and gas injection rates, bottom-hole pressures of the oil production wells, cycle ratio, cycle time, the composition of the injected hydrocarbon gas (miscible/immiscible WAG) and the total WAG period. In progressive experiments, the number of decision-making variables is increased, increasing the problem complexity while potentially improving the efficacy of the WAG process. The second objective function is selected to be the incremental recovery factor (IRF) within a fixed total WAG simulation time and it is optimised using the same optimisation algorithms. The results from the two optimisation techniques are analyzed and their performance, convergence speed and the quality of the optimal solutions found by the algorithms in multiple trials are compared for each experiment. The distinctions between the optimal WAG parameters resulting from NPV and oil recovery optimisation are also examined. This is the first known work optimising over this complete set of WAG variables. The first use of PSO to optimise a WAG project at the field scale is also illustrated. Compared to the reference cases, the best overall values of the objective functions found by GA and PSO were 13.8% and 14.2% higher, respectively, if NPV is optimised over all the above variables, and 14.2% and 16.2% higher, respectively, if IRF is optimised.